From 5cad133b1906721a23735f75f90c5e1387d9c181 Mon Sep 17 00:00:00 2001
From: juliagallucci <78810440+juliagallucci@users.noreply.github.com>
Date: Wed, 15 Apr 2026 17:05:18 -0400
Subject: [PATCH 1/4] Add files via upload

---
 .../live_code/live-code-4-15-2026.ipynb       | 5327 +++++++++++++++++
 1 file changed, 5327 insertions(+)
 create mode 100644 04_this_cohort/live_code/live-code-4-15-2026.ipynb

diff --git a/04_this_cohort/live_code/live-code-4-15-2026.ipynb b/04_this_cohort/live_code/live-code-4-15-2026.ipynb
new file mode 100644
index 000000000..3ce7afd2f
--- /dev/null
+++ b/04_this_cohort/live_code/live-code-4-15-2026.ipynb
@@ -0,0 +1,5327 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv(\"dataset/wdbc.csv\")\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant        17.99         10.38          122.80   \n",
+       "1      842517  Malignant        20.57         17.77          132.90   \n",
+       "2    84300903  Malignant        19.69         21.25          130.00   \n",
+       "3    84348301  Malignant        11.42         20.38           77.58   \n",
+       "4    84358402  Malignant        20.29         14.34          135.10   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant        21.56         22.39          142.00   \n",
+       "565    926682  Malignant        20.13         28.25          131.20   \n",
+       "566    926954  Malignant        16.60         28.08          108.30   \n",
+       "567    927241  Malignant        20.60         29.33          140.10   \n",
+       "568     92751     Benign         7.76         24.54           47.92   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0       1001.0          0.11840           0.27760         0.30010   \n",
+       "1       1326.0          0.08474           0.07864         0.08690   \n",
+       "2       1203.0          0.10960           0.15990         0.19740   \n",
+       "3        386.1          0.14250           0.28390         0.24140   \n",
+       "4       1297.0          0.10030           0.13280         0.19800   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564     1479.0          0.11100           0.11590         0.24390   \n",
+       "565     1261.0          0.09780           0.10340         0.14400   \n",
+       "566      858.1          0.08455           0.10230         0.09251   \n",
+       "567     1265.0          0.11780           0.27700         0.35140   \n",
+       "568      181.0          0.05263           0.04362         0.00000   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0                0.14710  ...        25.380          17.33           184.60   \n",
+       "1                0.07017  ...        24.990          23.41           158.80   \n",
+       "2                0.12790  ...        23.570          25.53           152.50   \n",
+       "3                0.10520  ...        14.910          26.50            98.87   \n",
+       "4                0.10430  ...        22.540          16.67           152.20   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564              0.13890  ...        25.450          26.40           166.10   \n",
+       "565              0.09791  ...        23.690          38.25           155.00   \n",
+       "566              0.05302  ...        18.980          34.12           126.70   \n",
+       "567              0.15200  ...        25.740          39.42           184.60   \n",
+       "568              0.00000  ...         9.456          30.37            59.16   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0        2019.0           0.16220            0.66560           0.7119   \n",
+       "1        1956.0           0.12380            0.18660           0.2416   \n",
+       "2        1709.0           0.14440            0.42450           0.4504   \n",
+       "3         567.7           0.20980            0.86630           0.6869   \n",
+       "4        1575.0           0.13740            0.20500           0.4000   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564      2027.0           0.14100            0.21130           0.4107   \n",
+       "565      1731.0           0.11660            0.19220           0.3215   \n",
+       "566      1124.0           0.11390            0.30940           0.3403   \n",
+       "567      1821.0           0.16500            0.86810           0.9387   \n",
+       "568       268.6           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"] = cancer[\"diagnosis\"].replace({\n",
+    "    \"M\": \"Malignant\",\n",
+    "    \"B\" : \"Benign\"\n",
+    "})\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardized_cancer = cancer.copy() #copy of dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['area_mean', 'area_se', 'area_worst', 'compactness_mean',\n",
+       "       'compactness_se', 'compactness_worst', 'concave points_mean',\n",
+       "       'concave points_se', 'concave points_worst', 'concavity_mean',\n",
+       "       'concavity_se', 'concavity_worst', 'fractal_dimension_mean',\n",
+       "       'fractal_dimension_se', 'fractal_dimension_worst', 'perimeter_mean',\n",
+       "       'perimeter_se', 'perimeter_worst', 'radius_mean', 'radius_se',\n",
+       "       'radius_worst', 'smoothness_mean', 'smoothness_se', 'smoothness_worst',\n",
+       "       'symmetry_mean', 'symmetry_se', 'symmetry_worst', 'texture_mean',\n",
+       "       'texture_se', 'texture_worst'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columns_to_exclude = ['id','diagnosis']\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)\n",
+    "columns_to_scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.097064</td>\n",
+       "      <td>-2.073335</td>\n",
+       "      <td>1.269934</td>\n",
+       "      <td>0.984375</td>\n",
+       "      <td>1.568466</td>\n",
+       "      <td>3.283515</td>\n",
+       "      <td>2.652874</td>\n",
+       "      <td>2.532475</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.886690</td>\n",
+       "      <td>-1.359293</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>2.001237</td>\n",
+       "      <td>1.307686</td>\n",
+       "      <td>2.616665</td>\n",
+       "      <td>2.109526</td>\n",
+       "      <td>2.296076</td>\n",
+       "      <td>2.750622</td>\n",
+       "      <td>1.937015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.829821</td>\n",
+       "      <td>-0.353632</td>\n",
+       "      <td>1.685955</td>\n",
+       "      <td>1.908708</td>\n",
+       "      <td>-0.826962</td>\n",
+       "      <td>-0.487072</td>\n",
+       "      <td>-0.023846</td>\n",
+       "      <td>0.548144</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.805927</td>\n",
+       "      <td>-0.369203</td>\n",
+       "      <td>1.535126</td>\n",
+       "      <td>1.890489</td>\n",
+       "      <td>-0.375612</td>\n",
+       "      <td>-0.430444</td>\n",
+       "      <td>-0.146749</td>\n",
+       "      <td>1.087084</td>\n",
+       "      <td>-0.243890</td>\n",
+       "      <td>0.281190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.579888</td>\n",
+       "      <td>0.456187</td>\n",
+       "      <td>1.566503</td>\n",
+       "      <td>1.558884</td>\n",
+       "      <td>0.942210</td>\n",
+       "      <td>1.052926</td>\n",
+       "      <td>1.363478</td>\n",
+       "      <td>2.037231</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.511870</td>\n",
+       "      <td>-0.023974</td>\n",
+       "      <td>1.347475</td>\n",
+       "      <td>1.456285</td>\n",
+       "      <td>0.527407</td>\n",
+       "      <td>1.082932</td>\n",
+       "      <td>0.854974</td>\n",
+       "      <td>1.955000</td>\n",
+       "      <td>1.152255</td>\n",
+       "      <td>0.201391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>-0.768909</td>\n",
+       "      <td>0.253732</td>\n",
+       "      <td>-0.592687</td>\n",
+       "      <td>-0.764464</td>\n",
+       "      <td>3.283553</td>\n",
+       "      <td>3.402909</td>\n",
+       "      <td>1.915897</td>\n",
+       "      <td>1.451707</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.281464</td>\n",
+       "      <td>0.133984</td>\n",
+       "      <td>-0.249939</td>\n",
+       "      <td>-0.550021</td>\n",
+       "      <td>3.394275</td>\n",
+       "      <td>3.893397</td>\n",
+       "      <td>1.989588</td>\n",
+       "      <td>2.175786</td>\n",
+       "      <td>6.046041</td>\n",
+       "      <td>4.935010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.750297</td>\n",
+       "      <td>-1.151816</td>\n",
+       "      <td>1.776573</td>\n",
+       "      <td>1.826229</td>\n",
+       "      <td>0.280372</td>\n",
+       "      <td>0.539340</td>\n",
+       "      <td>1.371011</td>\n",
+       "      <td>1.428493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.298575</td>\n",
+       "      <td>-1.466770</td>\n",
+       "      <td>1.338539</td>\n",
+       "      <td>1.220724</td>\n",
+       "      <td>0.220556</td>\n",
+       "      <td>-0.313395</td>\n",
+       "      <td>0.613179</td>\n",
+       "      <td>0.729259</td>\n",
+       "      <td>-0.868353</td>\n",
+       "      <td>-0.397100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>2.110995</td>\n",
+       "      <td>0.721473</td>\n",
+       "      <td>2.060786</td>\n",
+       "      <td>2.343856</td>\n",
+       "      <td>1.041842</td>\n",
+       "      <td>0.219060</td>\n",
+       "      <td>1.947285</td>\n",
+       "      <td>2.320965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.901185</td>\n",
+       "      <td>0.117700</td>\n",
+       "      <td>1.752563</td>\n",
+       "      <td>2.015301</td>\n",
+       "      <td>0.378365</td>\n",
+       "      <td>-0.273318</td>\n",
+       "      <td>0.664512</td>\n",
+       "      <td>1.629151</td>\n",
+       "      <td>-1.360158</td>\n",
+       "      <td>-0.709091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.704854</td>\n",
+       "      <td>2.085134</td>\n",
+       "      <td>1.615931</td>\n",
+       "      <td>1.723842</td>\n",
+       "      <td>0.102458</td>\n",
+       "      <td>-0.017833</td>\n",
+       "      <td>0.693043</td>\n",
+       "      <td>1.263669</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.536720</td>\n",
+       "      <td>2.047399</td>\n",
+       "      <td>1.421940</td>\n",
+       "      <td>1.494959</td>\n",
+       "      <td>-0.691230</td>\n",
+       "      <td>-0.394820</td>\n",
+       "      <td>0.236573</td>\n",
+       "      <td>0.733827</td>\n",
+       "      <td>-0.531855</td>\n",
+       "      <td>-0.973978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.702284</td>\n",
+       "      <td>2.045574</td>\n",
+       "      <td>0.672676</td>\n",
+       "      <td>0.577953</td>\n",
+       "      <td>-0.840484</td>\n",
+       "      <td>-0.038680</td>\n",
+       "      <td>0.046588</td>\n",
+       "      <td>0.105777</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.561361</td>\n",
+       "      <td>1.374854</td>\n",
+       "      <td>0.579001</td>\n",
+       "      <td>0.427906</td>\n",
+       "      <td>-0.809587</td>\n",
+       "      <td>0.350735</td>\n",
+       "      <td>0.326767</td>\n",
+       "      <td>0.414069</td>\n",
+       "      <td>-1.104549</td>\n",
+       "      <td>-0.318409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.838341</td>\n",
+       "      <td>2.336457</td>\n",
+       "      <td>1.982524</td>\n",
+       "      <td>1.735218</td>\n",
+       "      <td>1.525767</td>\n",
+       "      <td>3.272144</td>\n",
+       "      <td>3.296944</td>\n",
+       "      <td>2.658866</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.961239</td>\n",
+       "      <td>2.237926</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>1.653171</td>\n",
+       "      <td>1.430427</td>\n",
+       "      <td>3.904848</td>\n",
+       "      <td>3.197605</td>\n",
+       "      <td>2.289985</td>\n",
+       "      <td>1.919083</td>\n",
+       "      <td>2.219635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.808401</td>\n",
+       "      <td>1.221792</td>\n",
+       "      <td>-1.814389</td>\n",
+       "      <td>-1.347789</td>\n",
+       "      <td>-3.112085</td>\n",
+       "      <td>-1.150752</td>\n",
+       "      <td>-1.114873</td>\n",
+       "      <td>-1.261820</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.410893</td>\n",
+       "      <td>0.764190</td>\n",
+       "      <td>-1.432735</td>\n",
+       "      <td>-1.075813</td>\n",
+       "      <td>-1.859019</td>\n",
+       "      <td>-1.207552</td>\n",
+       "      <td>-1.305831</td>\n",
+       "      <td>-1.745063</td>\n",
+       "      <td>-0.048138</td>\n",
+       "      <td>-0.751207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant     1.097064     -2.073335        1.269934   \n",
+       "1      842517  Malignant     1.829821     -0.353632        1.685955   \n",
+       "2    84300903  Malignant     1.579888      0.456187        1.566503   \n",
+       "3    84348301  Malignant    -0.768909      0.253732       -0.592687   \n",
+       "4    84358402  Malignant     1.750297     -1.151816        1.776573   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant     2.110995      0.721473        2.060786   \n",
+       "565    926682  Malignant     1.704854      2.085134        1.615931   \n",
+       "566    926954  Malignant     0.702284      2.045574        0.672676   \n",
+       "567    927241  Malignant     1.838341      2.336457        1.982524   \n",
+       "568     92751     Benign    -1.808401      1.221792       -1.814389   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0     0.984375         1.568466          3.283515        2.652874   \n",
+       "1     1.908708        -0.826962         -0.487072       -0.023846   \n",
+       "2     1.558884         0.942210          1.052926        1.363478   \n",
+       "3    -0.764464         3.283553          3.402909        1.915897   \n",
+       "4     1.826229         0.280372          0.539340        1.371011   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564   2.343856         1.041842          0.219060        1.947285   \n",
+       "565   1.723842         0.102458         -0.017833        0.693043   \n",
+       "566   0.577953        -0.840484         -0.038680        0.046588   \n",
+       "567   1.735218         1.525767          3.272144        3.296944   \n",
+       "568  -1.347789        -3.112085         -1.150752       -1.114873   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0               2.532475  ...      1.886690      -1.359293         2.303601   \n",
+       "1               0.548144  ...      1.805927      -0.369203         1.535126   \n",
+       "2               2.037231  ...      1.511870      -0.023974         1.347475   \n",
+       "3               1.451707  ...     -0.281464       0.133984        -0.249939   \n",
+       "4               1.428493  ...      1.298575      -1.466770         1.338539   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564             2.320965  ...      1.901185       0.117700         1.752563   \n",
+       "565             1.263669  ...      1.536720       2.047399         1.421940   \n",
+       "566             0.105777  ...      0.561361       1.374854         0.579001   \n",
+       "567             2.658866  ...      1.961239       2.237926         2.303601   \n",
+       "568            -1.261820  ...     -1.410893       0.764190        -1.432735   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0      2.001237          1.307686           2.616665         2.109526   \n",
+       "1      1.890489         -0.375612          -0.430444        -0.146749   \n",
+       "2      1.456285          0.527407           1.082932         0.854974   \n",
+       "3     -0.550021          3.394275           3.893397         1.989588   \n",
+       "4      1.220724          0.220556          -0.313395         0.613179   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564    2.015301          0.378365          -0.273318         0.664512   \n",
+       "565    1.494959         -0.691230          -0.394820         0.236573   \n",
+       "566    0.427906         -0.809587           0.350735         0.326767   \n",
+       "567    1.653171          1.430427           3.904848         3.197605   \n",
+       "568   -1.075813         -1.859019          -1.207552        -1.305831   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                2.296076        2.750622                 1.937015  \n",
+       "1                1.087084       -0.243890                 0.281190  \n",
+       "2                1.955000        1.152255                 0.201391  \n",
+       "3                2.175786        6.046041                 4.935010  \n",
+       "4                0.729259       -0.868353                -0.397100  \n",
+       "..                    ...             ...                      ...  \n",
+       "564              1.629151       -1.360158                -0.709091  \n",
+       "565              0.733827       -0.531855                -0.973978  \n",
+       "566              0.414069       -1.104549                -0.318409  \n",
+       "567              2.289985        1.919083                 2.219635  \n",
+       "568             -1.745063       -0.048138                -0.751207  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler() #initialize our scaler\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(standardized_cancer[columns_to_scale])\n",
+    "standardized_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#set the seed\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "#split the data\n",
+    "cancer_train, cancer_test = train_test_split(\n",
+    "    standardized_cancer, train_size = 0.75, shuffle = True, stratify= standardized_cancer[\"diagnosis\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 426 entries, 164 to 284\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       426 non-null    int64  \n",
+      " 1   diagnosis                426 non-null    object \n",
+      " 2   radius_mean              426 non-null    float64\n",
+      " 3   texture_mean             426 non-null    float64\n",
+      " 4   perimeter_mean           426 non-null    float64\n",
+      " 5   area_mean                426 non-null    float64\n",
+      " 6   smoothness_mean          426 non-null    float64\n",
+      " 7   compactness_mean         426 non-null    float64\n",
+      " 8   concavity_mean           426 non-null    float64\n",
+      " 9   concave points_mean      426 non-null    float64\n",
+      " 10  symmetry_mean            426 non-null    float64\n",
+      " 11  fractal_dimension_mean   426 non-null    float64\n",
+      " 12  radius_se                426 non-null    float64\n",
+      " 13  texture_se               426 non-null    float64\n",
+      " 14  perimeter_se             426 non-null    float64\n",
+      " 15  area_se                  426 non-null    float64\n",
+      " 16  smoothness_se            426 non-null    float64\n",
+      " 17  compactness_se           426 non-null    float64\n",
+      " 18  concavity_se             426 non-null    float64\n",
+      " 19  concave points_se        426 non-null    float64\n",
+      " 20  symmetry_se              426 non-null    float64\n",
+      " 21  fractal_dimension_se     426 non-null    float64\n",
+      " 22  radius_worst             426 non-null    float64\n",
+      " 23  texture_worst            426 non-null    float64\n",
+      " 24  perimeter_worst          426 non-null    float64\n",
+      " 25  area_worst               426 non-null    float64\n",
+      " 26  smoothness_worst         426 non-null    float64\n",
+      " 27  compactness_worst        426 non-null    float64\n",
+      " 28  concavity_worst          426 non-null    float64\n",
+      " 29  concave points_worst     426 non-null    float64\n",
+      " 30  symmetry_worst           426 non-null    float64\n",
+      " 31  fractal_dimension_worst  426 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 109.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cancer_train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 143 entries, 357 to 332\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       143 non-null    int64  \n",
+      " 1   diagnosis                143 non-null    object \n",
+      " 2   radius_mean              143 non-null    float64\n",
+      " 3   texture_mean             143 non-null    float64\n",
+      " 4   perimeter_mean           143 non-null    float64\n",
+      " 5   area_mean                143 non-null    float64\n",
+      " 6   smoothness_mean          143 non-null    float64\n",
+      " 7   compactness_mean         143 non-null    float64\n",
+      " 8   concavity_mean           143 non-null    float64\n",
+      " 9   concave points_mean      143 non-null    float64\n",
+      " 10  symmetry_mean            143 non-null    float64\n",
+      " 11  fractal_dimension_mean   143 non-null    float64\n",
+      " 12  radius_se                143 non-null    float64\n",
+      " 13  texture_se               143 non-null    float64\n",
+      " 14  perimeter_se             143 non-null    float64\n",
+      " 15  area_se                  143 non-null    float64\n",
+      " 16  smoothness_se            143 non-null    float64\n",
+      " 17  compactness_se           143 non-null    float64\n",
+      " 18  concavity_se             143 non-null    float64\n",
+      " 19  concave points_se        143 non-null    float64\n",
+      " 20  symmetry_se              143 non-null    float64\n",
+      " 21  fractal_dimension_se     143 non-null    float64\n",
+      " 22  radius_worst             143 non-null    float64\n",
+      " 23  texture_worst            143 non-null    float64\n",
+      " 24  perimeter_worst          143 non-null    float64\n",
+      " 25  area_worst               143 non-null    float64\n",
+      " 26  smoothness_worst         143 non-null    float64\n",
+      " 27  compactness_worst        143 non-null    float64\n",
+      " 28  concavity_worst          143 non-null    float64\n",
+      " 29  concave points_worst     143 non-null    float64\n",
+      " 30  symmetry_worst           143 non-null    float64\n",
+      " 31  fractal_dimension_worst  143 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 36.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cancer_test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#K = 5\n",
+    "knn = KNeighborsClassifier(n_neighbors=5) #initialize our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#defining our X and y\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
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+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fit model to train data\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "    .dataframe thead th {\n",
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+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <td>Benign</td>\n",
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+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>901041</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.234963</td>\n",
+       "      <td>0.530653</td>\n",
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+       "      <td>-0.695035</td>\n",
+       "      <td>-0.636573</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>-0.724371</td>\n",
+       "      <td>-0.890242</td>\n",
+       "      <td>-0.426699</td>\n",
+       "      <td>-0.962341</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>8810703</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>3.971288</td>\n",
+       "      <td>-0.190738</td>\n",
+       "      <td>3.976130</td>\n",
+       "      <td>5.244841</td>\n",
+       "      <td>1.269571</td>\n",
+       "      <td>0.895628</td>\n",
+       "      <td>2.903973</td>\n",
+       "      <td>2.852321</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.173652</td>\n",
+       "      <td>2.419765</td>\n",
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+       "      <td>0.229857</td>\n",
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+       "      <td>-2.026684</td>\n",
+       "      <td>-1.590202</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
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+       "      <td>Benign</td>\n",
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+       "      <td>-1.633519</td>\n",
+       "      <td>-0.536668</td>\n",
+       "      <td>-0.530110</td>\n",
+       "      <td>-0.450497</td>\n",
+       "      <td>-0.782146</td>\n",
+       "      <td>-0.743497</td>\n",
+       "      <td>-0.579053</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.043377</td>\n",
+       "      <td>-0.596944</td>\n",
+       "      <td>-0.554943</td>\n",
+       "      <td>-0.138898</td>\n",
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+       "      <td>0.338511</td>\n",
+       "      <td>-0.444757</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
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+       "      <td>Benign</td>\n",
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+       "      <td>-0.729224</td>\n",
+       "      <td>-0.344455</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>9010877</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.206561</td>\n",
+       "      <td>-0.544452</td>\n",
+       "      <td>-0.267285</td>\n",
+       "      <td>-0.291490</td>\n",
+       "      <td>-1.209121</td>\n",
+       "      <td>-0.897940</td>\n",
+       "      <td>-0.841049</td>\n",
+       "      <td>-0.881874</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.647666</td>\n",
+       "      <td>-0.402145</td>\n",
+       "      <td>-0.381613</td>\n",
+       "      <td>-0.485202</td>\n",
+       "      <td>-0.551311</td>\n",
+       "      <td>-0.651448</td>\n",
+       "      <td>-0.681180</td>\n",
+       "      <td>-0.258450</td>\n",
+       "      <td>-0.450299</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
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+       "      <td>Benign</td>\n",
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+       "      <td>0.053500</td>\n",
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+       "      <td>-0.636788</td>\n",
+       "      <td>-0.694784</td>\n",
+       "      <td>-0.519727</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.836565</td>\n",
+       "      <td>-0.147774</td>\n",
+       "      <td>-0.181211</td>\n",
+       "      <td>-0.463284</td>\n",
+       "      <td>-0.631464</td>\n",
+       "      <td>-0.720533</td>\n",
+       "      <td>-0.531351</td>\n",
+       "      <td>-0.607890</td>\n",
+       "      <td>-0.868688</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>892399</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.027362</td>\n",
+       "      <td>0.884367</td>\n",
+       "      <td>-1.034658</td>\n",
+       "      <td>-0.912073</td>\n",
+       "      <td>0.365770</td>\n",
+       "      <td>-0.689284</td>\n",
+       "      <td>-0.801626</td>\n",
+       "      <td>-0.778182</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.237300</td>\n",
+       "      <td>-1.106878</td>\n",
+       "      <td>-0.910393</td>\n",
+       "      <td>-0.792053</td>\n",
+       "      <td>-1.069510</td>\n",
+       "      <td>-1.106350</td>\n",
+       "      <td>-1.269232</td>\n",
+       "      <td>-1.089989</td>\n",
+       "      <td>-0.896396</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>913512</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.695066</td>\n",
+       "      <td>-0.725963</td>\n",
+       "      <td>-0.678775</td>\n",
+       "      <td>-0.666627</td>\n",
+       "      <td>1.169940</td>\n",
+       "      <td>-0.221940</td>\n",
+       "      <td>-0.577646</td>\n",
+       "      <td>-0.453952</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.665579</td>\n",
+       "      <td>-0.616305</td>\n",
+       "      <td>-0.581488</td>\n",
+       "      <td>0.886862</td>\n",
+       "      <td>-0.677903</td>\n",
+       "      <td>-0.591000</td>\n",
+       "      <td>-0.250572</td>\n",
+       "      <td>-0.156530</td>\n",
+       "      <td>-0.205361</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>897132</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.825712</td>\n",
+       "      <td>0.132725</td>\n",
+       "      <td>-0.825000</td>\n",
+       "      <td>-0.761051</td>\n",
+       "      <td>0.643316</td>\n",
+       "      <td>-0.692695</td>\n",
+       "      <td>-1.052023</td>\n",
+       "      <td>-1.066224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.016737</td>\n",
+       "      <td>-0.904036</td>\n",
+       "      <td>-0.781363</td>\n",
+       "      <td>0.439736</td>\n",
+       "      <td>-1.002397</td>\n",
+       "      <td>-1.241784</td>\n",
+       "      <td>-1.437181</td>\n",
+       "      <td>0.632947</td>\n",
+       "      <td>-1.037706</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>143 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "357    901028     Benign    -0.073075     -0.716655       -0.142066   \n",
+       "361    901041     Benign    -0.234963      0.530653       -0.277170   \n",
+       "212   8810703  Malignant     3.971288     -0.190738        3.976130   \n",
+       "527  91813702     Benign    -0.507616     -1.633519       -0.536668   \n",
+       "21    8510824     Benign    -1.313080     -1.593959       -1.302806   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "364   9010877     Benign    -0.206561     -0.544452       -0.267285   \n",
+       "434    908469     Benign     0.208100     -0.546779        0.120315   \n",
+       "299    892399     Benign    -1.027362      0.884367       -1.034658   \n",
+       "488    913512     Benign    -0.695066     -0.725963       -0.678775   \n",
+       "332    897132     Benign    -0.825712      0.132725       -0.825000   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "357  -0.174028        -0.635527         -0.936601       -0.926297   \n",
+       "361  -0.309407        -0.750104         -0.769638       -0.695035   \n",
+       "212   5.244841         1.269571          0.895628        2.903973   \n",
+       "527  -0.530110        -0.450497         -0.782146       -0.743497   \n",
+       "21   -1.083572         0.429819         -0.747086       -0.743748   \n",
+       "..         ...              ...               ...             ...   \n",
+       "364  -0.291490        -1.209121         -0.897940       -0.841049   \n",
+       "434   0.053500        -0.506718         -0.636788       -0.694784   \n",
+       "299  -0.912073         0.365770         -0.689284       -0.801626   \n",
+       "488  -0.666627         1.169940         -0.221940       -0.577646   \n",
+       "332  -0.761051         0.643316         -0.692695       -1.052023   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "357            -0.723241  ...      -0.015832        -0.313383   -0.327294   \n",
+       "361            -0.636573  ...       0.573662        -0.426569   -0.455973   \n",
+       "212             2.852321  ...      -1.173652         2.419765    2.845036   \n",
+       "527            -0.579053  ...      -1.043377        -0.596944   -0.554943   \n",
+       "21             -0.726337  ...      -1.631243        -1.254913   -0.994422   \n",
+       "..                   ...  ...            ...              ...         ...   \n",
+       "364            -0.881874  ...      -0.647666        -0.402145   -0.381613   \n",
+       "434            -0.519727  ...      -0.836565        -0.147774   -0.181211   \n",
+       "299            -0.778182  ...      -0.237300        -1.106878   -0.910393   \n",
+       "488            -0.453952  ...      -0.665579        -0.616305   -0.581488   \n",
+       "332            -1.066224  ...       0.016737        -0.904036   -0.781363   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "357         -0.748217          -0.976252        -1.052282   \n",
+       "361         -0.805204          -0.557036        -0.724371   \n",
+       "212         -0.796437          -0.653093         0.229857   \n",
+       "527         -0.138898          -0.298127        -0.446594   \n",
+       "21           0.001377          -0.887193        -0.880434   \n",
+       "..                ...                ...              ...   \n",
+       "364         -0.485202          -0.551311        -0.651448   \n",
+       "434         -0.463284          -0.631464        -0.720533   \n",
+       "299         -0.792053          -1.069510        -1.106350   \n",
+       "488          0.886862          -0.677903        -0.591000   \n",
+       "332          0.439736          -1.002397        -1.241784   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  predicted  \n",
+       "357             -0.899073       -0.871589                -0.710199     Benign  \n",
+       "361             -0.890242       -0.426699                -0.962341     Benign  \n",
+       "212              0.683579       -2.026684                -1.590202  Malignant  \n",
+       "527             -0.115817        0.338511                -0.444757     Benign  \n",
+       "21              -0.796903       -0.729224                -0.344455     Benign  \n",
+       "..                    ...             ...                      ...        ...  \n",
+       "364             -0.681180       -0.258450                -0.450299     Benign  \n",
+       "434             -0.531351       -0.607890                -0.868688     Benign  \n",
+       "299             -1.269232       -1.089989                -0.896396     Benign  \n",
+       "488             -0.250572       -0.156530                -0.205361     Benign  \n",
+       "332             -1.437181        0.632947                -1.037706     Benign  \n",
+       "\n",
+       "[143 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_test[\"predicted\"] = knn.predict(cancer_test[[\"perimeter_mean\",\"concavity_mean\"]])\n",
+    "cancer_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9230769230769231"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(cancer_test[[\"perimeter_mean\",\"concavity_mean\"]],\n",
+    "          cancer_test[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>predicted</th>\n",
+       "      <th>Benign</th>\n",
+       "      <th>Malignant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Benign</th>\n",
+       "      <td>88</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Malignant</th>\n",
+       "      <td>9</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "predicted  Benign  Malignant\n",
+       "diagnosis                   \n",
+       "Benign         88          2\n",
+       "Malignant       9         44"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(\n",
+    "   cancer_test[\"diagnosis\"], #actual labels \n",
+    "   cancer_test[\"predicted\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9565217391304348"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(\n",
+    "  y_true = cancer_test[\"diagnosis\"], #actual label  \n",
+    "  y_pred = cancer_test[\"predicted\"], #predicted label\n",
+    "  pos_label = \"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8301886792452831"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(\n",
+    "   y_true= cancer_test[\"diagnosis\"],\n",
+    "   y_pred = cancer_test[\"predicted\"],\n",
+    "   pos_label = \"Malignant\" \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#subdivide training data into train and validation\n",
+    "cancer_subtrain, cancer_validation = train_test_split(cancer_train, train_size=0.75, stratify=cancer_train[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#k = 3 train on subtraining data\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_subtrain[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_subtrain[\"diagnosis\"]\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.897196261682243"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#compute the score on validation data\n",
+    "knn.score(\n",
+    "    cancer_validation[[\"perimeter_mean\",\"concavity_mean\"]],\n",
+    "    cancer_validation[\"diagnosis\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train['diagnosis']\n",
+    "\n",
+    "returned_dictionary = cross_validate(\n",
+    "  estimator = knn,\n",
+    "  cv = 5, # 5-fold cross validation\n",
+    "  X = X,\n",
+    "  y = y  \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001281</td>\n",
+       "      <td>0.003107</td>\n",
+       "      <td>0.930233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.002080</td>\n",
+       "      <td>0.001183</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000999</td>\n",
+       "      <td>0.001682</td>\n",
+       "      <td>0.870588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.001020</td>\n",
+       "      <td>0.001634</td>\n",
+       "      <td>0.952941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000967</td>\n",
+       "      <td>0.002174</td>\n",
+       "      <td>0.917647</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time  test_score\n",
+       "0  0.001281    0.003107    0.930233\n",
+       "1  0.002080    0.001183    0.894118\n",
+       "2  0.000999    0.001682    0.870588\n",
+       "3  0.001020    0.001634    0.952941\n",
+       "4  0.000967    0.002174    0.917647"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df = pd.DataFrame(returned_dictionary)\n",
+    "cv_5_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.001269</td>\n",
+       "      <td>0.001956</td>\n",
+       "      <td>0.913105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.000210</td>\n",
+       "      <td>0.000328</td>\n",
+       "      <td>0.014264</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time  test_score\n",
+       "mean  0.001269    0.001956    0.913105\n",
+       "sem   0.000210    0.000328    0.014264"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df.agg([\"mean\",\"sem\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 1. define parameter grid and initize empty model\n",
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1,400,5)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 2. initialize our grid search object\n",
+    "cancer_tune_grid = GridSearchCV(\n",
+    "    estimator = knn,\n",
+    "    param_grid= parameter_grid,\n",
+    "    cv = 10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 386, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 391, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 383, n_samples = 43\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:953: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 942, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test, **score_params)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/metrics/_scorer.py\", line 492, in __call__\n",
+      "    return estimator.score(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 446, in score\n",
+      "    return super().score(X, y, sample_weight)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/base.py\", line 548, in score\n",
+      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
+      "                             ^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_classification.py\", line 274, in predict\n",
+      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/neighbors/_base.py\", line 854, in kneighbors\n",
+      "    raise ValueError(\n",
+      "ValueError: Expected n_neighbors <= n_samples_fit, but n_neighbors = 396, n_samples_fit = 384, n_samples = 42\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/juliagallucci/Desktop/CI:TA Positions/DSI Linear regression, classification, and resampling CI/LCR/lcr-env/lib/python3.11/site-packages/sklearn/model_selection/_search.py:1135: UserWarning: One or more of the test scores are non-finite: [0.87569214 0.90863787 0.90404208 0.92048726 0.91339978 0.91339978\n",
+      " 0.91339978 0.9110742  0.9110742  0.91578073 0.91345515 0.92043189\n",
+      " 0.91810631 0.92043189 0.92043189 0.92043189 0.92043189 0.91572536\n",
+      " 0.91334441 0.91334441 0.91334441 0.90863787 0.90863787 0.90863787\n",
+      " 0.90863787 0.90863787 0.90863787 0.90620155 0.9038206  0.90614618\n",
+      " 0.90143965 0.89905869 0.89435216 0.89435216 0.89202658 0.88493909\n",
+      " 0.88028793 0.88028793 0.87790698 0.87785161 0.87785161 0.87552602\n",
+      " 0.88017719 0.87547065 0.87547065 0.8730897  0.8730897  0.8730897\n",
+      " 0.87076412 0.8521041  0.84269103 0.8286268  0.8192691  0.8051495\n",
+      " 0.77220377 0.73460687 0.70885936 0.62674419 0.62674419 0.62674419\n",
+      " 0.62674419 0.62674419 0.62674419 0.62674419 0.62674419 0.62674419\n",
+      " 0.62674419 0.62674419 0.62674419 0.62674419 0.62674419 0.62674419\n",
+      " 0.62674419 0.62674419 0.62674419 0.62674419 0.62674419        nan\n",
+      "        nan        nan]\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 400, 5)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">KNeighborsClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">param_grid&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 400, 5)}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">scoring&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">refit&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cv&nbsp;</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">error_score&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(n_neighbors=16)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">16</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={'n_neighbors': range(1, 400, 5)})"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#step 3. fit model to our train data\n",
+    "cancer_tune_grid.fit(\n",
+    "    cancer_train[[\"perimeter_mean\",\"concavity_mean\"]],\n",
+    "    cancer_train[\"diagnosis\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>split5_test_score</th>\n",
+       "      <th>split6_test_score</th>\n",
+       "      <th>split7_test_score</th>\n",
+       "      <th>split8_test_score</th>\n",
+       "      <th>split9_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001179</td>\n",
+       "      <td>0.000420</td>\n",
+       "      <td>0.001785</td>\n",
+       "      <td>0.000775</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.875692</td>\n",
+       "      <td>0.038684</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.000864</td>\n",
+       "      <td>0.000182</td>\n",
+       "      <td>0.001191</td>\n",
+       "      <td>0.000210</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{'n_neighbors': 6}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.908638</td>\n",
+       "      <td>0.043373</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000922</td>\n",
+       "      <td>0.000172</td>\n",
+       "      <td>0.001283</td>\n",
+       "      <td>0.000151</td>\n",
+       "      <td>11</td>\n",
+       "      <td>{'n_neighbors': 11}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904042</td>\n",
+       "      <td>0.039147</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000976</td>\n",
+       "      <td>0.000189</td>\n",
+       "      <td>0.001346</td>\n",
+       "      <td>0.000268</td>\n",
+       "      <td>16</td>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920487</td>\n",
+       "      <td>0.045315</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000903</td>\n",
+       "      <td>0.000154</td>\n",
+       "      <td>0.001249</td>\n",
+       "      <td>0.000133</td>\n",
+       "      <td>21</td>\n",
+       "      <td>{'n_neighbors': 21}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.046495</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>0.001833</td>\n",
+       "      <td>0.000311</td>\n",
+       "      <td>0.005214</td>\n",
+       "      <td>0.000782</td>\n",
+       "      <td>376</td>\n",
+       "      <td>{'n_neighbors': 376}</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.619048</td>\n",
+       "      <td>0.619048</td>\n",
+       "      <td>0.619048</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.626744</td>\n",
+       "      <td>0.006674</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>0.001776</td>\n",
+       "      <td>0.000224</td>\n",
+       "      <td>0.005188</td>\n",
+       "      <td>0.000859</td>\n",
+       "      <td>381</td>\n",
+       "      <td>{'n_neighbors': 381}</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.627907</td>\n",
+       "      <td>0.619048</td>\n",
+       "      <td>0.619048</td>\n",
+       "      <td>0.619048</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.626744</td>\n",
+       "      <td>0.006674</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>0.001567</td>\n",
+       "      <td>0.000202</td>\n",
+       "      <td>0.001943</td>\n",
+       "      <td>0.002271</td>\n",
+       "      <td>386</td>\n",
+       "      <td>{'n_neighbors': 386}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>0.001930</td>\n",
+       "      <td>0.000330</td>\n",
+       "      <td>0.001491</td>\n",
+       "      <td>0.000797</td>\n",
+       "      <td>391</td>\n",
+       "      <td>{'n_neighbors': 391}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>0.001906</td>\n",
+       "      <td>0.000495</td>\n",
+       "      <td>0.001484</td>\n",
+       "      <td>0.000461</td>\n",
+       "      <td>396</td>\n",
+       "      <td>{'n_neighbors': 396}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>80 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.001179      0.000420         0.001785        0.000775   \n",
+       "1        0.000864      0.000182         0.001191        0.000210   \n",
+       "2        0.000922      0.000172         0.001283        0.000151   \n",
+       "3        0.000976      0.000189         0.001346        0.000268   \n",
+       "4        0.000903      0.000154         0.001249        0.000133   \n",
+       "..            ...           ...              ...             ...   \n",
+       "75       0.001833      0.000311         0.005214        0.000782   \n",
+       "76       0.001776      0.000224         0.005188        0.000859   \n",
+       "77       0.001567      0.000202         0.001943        0.002271   \n",
+       "78       0.001930      0.000330         0.001491        0.000797   \n",
+       "79       0.001906      0.000495         0.001484        0.000461   \n",
+       "\n",
+       "    param_n_neighbors                params  split0_test_score  \\\n",
+       "0                   1    {'n_neighbors': 1}           0.953488   \n",
+       "1                   6    {'n_neighbors': 6}           0.930233   \n",
+       "2                  11   {'n_neighbors': 11}           0.906977   \n",
+       "3                  16   {'n_neighbors': 16}           0.906977   \n",
+       "4                  21   {'n_neighbors': 21}           0.906977   \n",
+       "..                ...                   ...                ...   \n",
+       "75                376  {'n_neighbors': 376}           0.627907   \n",
+       "76                381  {'n_neighbors': 381}           0.627907   \n",
+       "77                386  {'n_neighbors': 386}                NaN   \n",
+       "78                391  {'n_neighbors': 391}                NaN   \n",
+       "79                396  {'n_neighbors': 396}                NaN   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0            0.837209           0.906977           0.860465   \n",
+       "1            0.953488           0.906977           0.837209   \n",
+       "2            0.930233           0.906977           0.837209   \n",
+       "3            0.953488           0.930233           0.837209   \n",
+       "4            0.953488           0.930233           0.837209   \n",
+       "..                ...                ...                ...   \n",
+       "75           0.627907           0.627907           0.627907   \n",
+       "76           0.627907           0.627907           0.627907   \n",
+       "77                NaN                NaN                NaN   \n",
+       "78                NaN                NaN                NaN   \n",
+       "79                NaN                NaN                NaN   \n",
+       "\n",
+       "    split4_test_score  split5_test_score  split6_test_score  \\\n",
+       "0            0.813953           0.837209           0.880952   \n",
+       "1            0.837209           0.906977           0.928571   \n",
+       "2            0.837209           0.883721           0.952381   \n",
+       "3            0.837209           0.930233           0.952381   \n",
+       "4            0.837209           0.906977           0.952381   \n",
+       "..                ...                ...                ...   \n",
+       "75           0.627907           0.627907           0.619048   \n",
+       "76           0.627907           0.627907           0.619048   \n",
+       "77                NaN                NaN                NaN   \n",
+       "78                NaN                NaN                NaN   \n",
+       "79                NaN                NaN                NaN   \n",
+       "\n",
+       "    split7_test_score  split8_test_score  split9_test_score  mean_test_score  \\\n",
+       "0            0.904762           0.880952           0.880952         0.875692   \n",
+       "1            0.928571           0.880952           0.976190         0.908638   \n",
+       "2            0.904762           0.928571           0.952381         0.904042   \n",
+       "3            0.952381           0.928571           0.976190         0.920487   \n",
+       "4            0.952381           0.880952           0.976190         0.913400   \n",
+       "..                ...                ...                ...              ...   \n",
+       "75           0.619048           0.619048           0.642857         0.626744   \n",
+       "76           0.619048           0.619048           0.642857         0.626744   \n",
+       "77                NaN                NaN                NaN              NaN   \n",
+       "78                NaN                NaN                NaN              NaN   \n",
+       "79                NaN                NaN                NaN              NaN   \n",
+       "\n",
+       "    std_test_score  rank_test_score  \n",
+       "0         0.038684               42  \n",
+       "1         0.043373               19  \n",
+       "2         0.039147               28  \n",
+       "3         0.045315                1  \n",
+       "4         0.046495               11  \n",
+       "..             ...              ...  \n",
+       "75        0.006674               58  \n",
+       "76        0.006674               58  \n",
+       "77             NaN               78  \n",
+       "78             NaN               78  \n",
+       "79             NaN               78  \n",
+       "\n",
+       "[80 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_grid = pd.DataFrame(cancer_tune_grid.cv_results_)\n",
+    "accuracy_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(accuracy_grid['param_n_neighbors'], accuracy_grid['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 16}"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#best value of K\n",
+    "cancer_tune_grid.best_params_"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 4471503f100c168f3be55094ada54fb55fc74bc4 Mon Sep 17 00:00:00 2001
From: juliagallucci <78810440+juliagallucci@users.noreply.github.com>
Date: Wed, 15 Apr 2026 17:42:57 -0400
Subject: [PATCH 2/4] Update README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 90fcda70e..58ae225f6 100644
--- a/README.md
+++ b/README.md
@@ -45,9 +45,9 @@ Participants should review the [Assignment Submission Guide](https://github.com/
 
 | Assessment   | Content                            | Due Dates   | 
 |--------------|------------------------------------|--------------|
-| Assignment 1 | Classification (Sessions 1, 2)     | Apr 19 | 
-| Assignment 2 | Regression (Sessions 3, 4)         | Apr 28    |
-| Assignment 3 | Clustering & Resampling (Sessions 5, 6) | May 5   |
+| Assignment 1 | Classification (Sessions 1, 2)     | Apr 21 | 
+| Assignment 2 | Regression (Sessions 3, 4)         | May 3    |
+| Assignment 3 | Clustering & Resampling (Sessions 5, 6) | May 10   |
 
 ### Contacts
 **Questions can be submitted to the _#dc3-help_ channel on Slack**

From 844ba7e017f1400008b4b3bbbd1f14257c9967dd Mon Sep 17 00:00:00 2001
From: Aunima <aunima.bhuiya@mail.utoronto.ca>
Date: Sun, 19 Apr 2026 22:29:21 -0400
Subject: [PATCH 3/4] Complete assignment 1

---
 02_activities/assignments/assignment_1.ipynb | 1445 +++++++++++++++++-
 1 file changed, 1410 insertions(+), 35 deletions(-)

diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 1d25bbcb3..a2d793d1d 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "178\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "print(wine_df.shape[0])"
    ]
   },
   {
@@ -109,12 +396,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 178 entries, 0 to 177\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       178 non-null    float64\n",
+      " 1   malic_acid                    178 non-null    float64\n",
+      " 2   ash                           178 non-null    float64\n",
+      " 3   alcalinity_of_ash             178 non-null    float64\n",
+      " 4   magnesium                     178 non-null    float64\n",
+      " 5   total_phenols                 178 non-null    float64\n",
+      " 6   flavanoids                    178 non-null    float64\n",
+      " 7   nonflavanoid_phenols          178 non-null    float64\n",
+      " 8   proanthocyanins               178 non-null    float64\n",
+      " 9   color_intensity               178 non-null    float64\n",
+      " 10  hue                           178 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  178 non-null    float64\n",
+      " 12  proline                       178 non-null    float64\n",
+      " 13  class                         178 non-null    int64  \n",
+      "dtypes: float64(13), int64(1)\n",
+      "memory usage: 19.6 KB\n",
+      "None\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "print(wine_df.info())"
    ]
   },
   {
@@ -127,12 +444,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "int64\n",
+      "[0 1 2]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "print(wine_df['class'].dtype)\n",
+    "print(wine_df[\"class\"].unique())"
    ]
   },
   {
@@ -146,12 +474,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "print(wine_df.shape[1]-1)"
    ]
   },
   {
@@ -175,10 +512,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "cc899b59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
    "source": [
     "# Select predictors (excluding the last column)\n",
     "predictors = wine_df.iloc[:, :-1]\n",
@@ -204,7 +568,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> It is important for KNN because it is based on distance. Without standardization, it could incorrectly identify nearest neighbours. Therefore, standardization helps to ensure each feature is equal to the distance between wine samples"
    ]
   },
   {
@@ -220,7 +584,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> The variable \"class\" is categorical, therefore not quantities with meaningful magnitude"
    ]
   },
   {
@@ -236,7 +600,7 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> It is to ensure that splitting of the data (training and testing) each time we run the code. No, the seed value does not matter because the value does not impact the model, but rather it is important to fix it to one value"
    ]
   },
   {
@@ -251,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
@@ -261,7 +625,10 @@
     "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
     "\n",
-    "# Your code here ..."
+    "# Your code here ...\n",
+    "x_train, x_test, y_train, y_test = train_test_split(\n",
+    "    predictors_standardized, wine_df[\"class\"], train_size = 0.75, shuffle = True, stratify= wine_df[\"class\"], random_state = 123\n",
+    ")"
    ]
   },
   {
@@ -284,12 +651,1011 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 51)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">KNeighborsClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
+       "            param_grid\n",
+       "            <span class=\"param-doc-description\">param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 51)}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
+       "            scoring\n",
+       "            <span class=\"param-doc-description\">scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`'s<br>  :ref:`default evaluation criterion <scoring_api_overview>` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
+       "            refit\n",
+       "            <span class=\"param-doc-description\">refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br><sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py>`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
+       "            cv\n",
+       "            <span class=\"param-doc-description\">cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int<br><br>Controls the verbosity: the higher, the more messages.<br><br>- >1 : the computation time for each fold and parameter candidate is<br>  displayed;<br>- >2 : the score is also displayed;<br>- >3 : the fold and candidate parameter indexes are also displayed<br>  together with the starting time of the computation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
+       "            pre_dispatch\n",
+       "            <span class=\"param-doc-description\">pre_dispatch: int, or str, default='2*n_jobs'<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in '2*n_jobs'</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
+       "            error_score\n",
+       "            <span class=\"param-doc-description\">error_score: 'raise' or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to 'raise', the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
+       "            return_train_score\n",
+       "            <span class=\"param-doc-description\">return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">7</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-3');</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={'n_neighbors': range(1, 51)})"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Your code here...\n",
+    "knn = KNeighborsClassifier()\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1, 51)\n",
+    "}\n",
+    "wine_tune_grid = GridSearchCV(\n",
+    "    estimator = knn,\n",
+    "    param_grid = parameter_grid,\n",
+    "    cv = 10 \n",
+    ")\n",
+    "wine_tune_grid.fit(x_train, y_train)"
    ]
   },
   {
@@ -305,12 +1671,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final Test Accuracy: 93.33%\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Your code here...\n",
+    "best_n_neighbors = wine_tune_grid.best_params_['n_neighbors']\n",
+    "knn_best = KNeighborsClassifier(n_neighbors = best_n_neighbors)\n",
+    "knn.fit(x_train, y_train)\n",
+    "y_pred = knn.predict(x_test)\n",
+    "final_accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f\"Final Test Accuracy: {final_accuracy:.2%}\")"
    ]
   },
   {
@@ -365,7 +1745,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +1759,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,

From a78ed95042a5f630fa1cec31f95568b63556cc57 Mon Sep 17 00:00:00 2001
From: Aunima <aunima.bhuiya@mail.utoronto.ca>
Date: Mon, 27 Apr 2026 12:43:49 -0400
Subject: [PATCH 4/4] assignment-1

---
 02_activities/assignments/assignment_1.ipynb | 1094 +-----------------
 1 file changed, 38 insertions(+), 1056 deletions(-)

diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index a2d793d1d..0422bee27 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
    "outputs": [
@@ -291,49 +291,23 @@
        "</div>"
       ],
       "text/plain": [
-       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
-       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
-       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
-       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
-       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
-       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
-       "..       ...         ...   ...                ...        ...            ...   \n",
-       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
-       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
-       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
-       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
-       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
-       "\n",
-       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
-       "0          3.06                  0.28             2.29             5.64  1.04   \n",
-       "1          2.76                  0.26             1.28             4.38  1.05   \n",
-       "2          3.24                  0.30             2.81             5.68  1.03   \n",
-       "3          3.49                  0.24             2.18             7.80  0.86   \n",
-       "4          2.69                  0.39             1.82             4.32  1.04   \n",
-       "..          ...                   ...              ...              ...   ...   \n",
-       "173        0.61                  0.52             1.06             7.70  0.64   \n",
-       "174        0.75                  0.43             1.41             7.30  0.70   \n",
-       "175        0.69                  0.43             1.35            10.20  0.59   \n",
-       "176        0.68                  0.53             1.46             9.30  0.60   \n",
-       "177        0.76                  0.56             1.35             9.20  0.61   \n",
-       "\n",
-       "     od280/od315_of_diluted_wines  proline  class  \n",
-       "0                            3.92   1065.0      0  \n",
-       "1                            3.40   1050.0      0  \n",
-       "2                            3.17   1185.0      0  \n",
-       "3                            3.45   1480.0      0  \n",
-       "4                            2.93    735.0      0  \n",
-       "..                            ...      ...    ...  \n",
-       "173                          1.74    740.0      2  \n",
-       "174                          1.56    750.0      2  \n",
-       "175                          1.56    835.0      2  \n",
-       "176                          1.62    840.0      2  \n",
-       "177                          1.60    560.0      2  \n",
+       "     alcohol  malic_acid   ash  ...  od280/od315_of_diluted_wines  proline  class\n",
+       "0      14.23        1.71  2.43  ...                          3.92   1065.0      0\n",
+       "1      13.20        1.78  2.14  ...                          3.40   1050.0      0\n",
+       "2      13.16        2.36  2.67  ...                          3.17   1185.0      0\n",
+       "3      14.37        1.95  2.50  ...                          3.45   1480.0      0\n",
+       "4      13.24        2.59  2.87  ...                          2.93    735.0      0\n",
+       "..       ...         ...   ...  ...                           ...      ...    ...\n",
+       "173    13.71        5.65  2.45  ...                          1.74    740.0      2\n",
+       "174    13.40        3.91  2.48  ...                          1.56    750.0      2\n",
+       "175    13.27        4.28  2.26  ...                          1.56    835.0      2\n",
+       "176    13.17        2.59  2.37  ...                          1.62    840.0      2\n",
+       "177    14.13        4.10  2.74  ...                          1.60    560.0      2\n",
        "\n",
        "[178 rows x 14 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "id": "56916892",
    "metadata": {},
    "outputs": [
@@ -396,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "df0ef103",
    "metadata": {},
    "outputs": [
@@ -474,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "id": "bd7b0910",
    "metadata": {},
    "outputs": [
@@ -512,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "id": "cc899b59",
    "metadata": {},
    "outputs": [
@@ -520,26 +494,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
-      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
-      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
-      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
-      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
-      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "    alcohol  malic_acid  ...  od280/od315_of_diluted_wines   proline\n",
+      "0  1.518613   -0.562250  ...                      1.847920  1.013009\n",
+      "1  0.246290   -0.499413  ...                      1.113449  0.965242\n",
+      "2  0.196879    0.021231  ...                      0.788587  1.395148\n",
+      "3  1.691550   -0.346811  ...                      1.184071  2.334574\n",
+      "4  0.295700    0.227694  ...                      0.449601 -0.037874\n",
       "\n",
-      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
-      "0       0.808997    1.034819             -0.659563         1.224884   \n",
-      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
-      "2       0.808997    1.215533             -0.498407         2.135968   \n",
-      "3       2.491446    1.466525             -0.981875         1.032155   \n",
-      "4       0.808997    0.663351              0.226796         0.401404   \n",
-      "\n",
-      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
-      "0         0.251717  0.362177                      1.847920  1.013009  \n",
-      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
-      "2         0.269020  0.318304                      0.788587  1.395148  \n",
-      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
-      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+      "[5 rows x 13 columns]\n"
      ]
     }
    ],
@@ -568,7 +530,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> It is important for KNN because it is based on distance. Without standardization, it could incorrectly identify nearest neighbours. Therefore, standardization helps to ensure each feature is equal to the distance between wine samples"
+    "Without standardization, features with larger scales can dominate the distance and bias the identification of nearest neighbours. Standardization ensures that all features are on a similar scale, so each feature contributes fairly."
    ]
   },
   {
@@ -600,7 +562,7 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> It is to ensure that splitting of the data (training and testing) each time we run the code. No, the seed value does not matter because the value does not impact the model, but rather it is important to fix it to one value"
+    "Setting a seed is important because it ensures that the random processes (like data splitting or random initialization) produce the same results each time the code is run, which makes experiments reproducible."
    ]
   },
   {
@@ -615,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 15,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
@@ -651,997 +613,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 21,
    "id": "08818c64",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
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-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
-       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 51)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('estimator',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
-       "            estimator\n",
-       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">KNeighborsClassifier()</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('param_grid',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
-       "            param_grid\n",
-       "            <span class=\"param-doc-description\">param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 51)}</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('scoring',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
-       "            scoring\n",
-       "            <span class=\"param-doc-description\">scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`'s<br>  :ref:`default evaluation criterion <scoring_api_overview>` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('refit',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
-       "            refit\n",
-       "            <span class=\"param-doc-description\">refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br><sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py>`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('cv',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
-       "            cv\n",
-       "            <span class=\"param-doc-description\">cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">10</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\">verbose: int<br><br>Controls the verbosity: the higher, the more messages.<br><br>- >1 : the computation time for each fold and parameter candidate is<br>  displayed;<br>- >2 : the score is also displayed;<br>- >3 : the fold and candidate parameter indexes are also displayed<br>  together with the starting time of the computation.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
-       "            pre_dispatch\n",
-       "            <span class=\"param-doc-description\">pre_dispatch: int, or str, default='2*n_jobs'<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in '2*n_jobs'</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('error_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
-       "            error_score\n",
-       "            <span class=\"param-doc-description\">error_score: 'raise' or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to 'raise', the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">nan</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('return_train_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
-       "            return_train_score\n",
-       "            <span class=\"param-doc-description\">return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_neighbors',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
-       "            n_neighbors\n",
-       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">7</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('weights',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
-       "            weights\n",
-       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('algorithm',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
-       "            algorithm\n",
-       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('leaf_size',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
-       "            leaf_size\n",
-       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">30</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('p',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
-       "            p\n",
-       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('metric',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
-       "            metric\n",
-       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('metric_params',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
-       "            metric_params\n",
-       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
-       "             param_grid={'n_neighbors': range(1, 51)})"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'n_neighbors': 7}\n"
+     ]
     }
    ],
    "source": [
@@ -1655,7 +636,8 @@
     "    param_grid = parameter_grid,\n",
     "    cv = 10 \n",
     ")\n",
-    "wine_tune_grid.fit(x_train, y_train)"
+    "wine_tune_grid.fit(x_train, y_train)\n",
+    "print (wine_tune_grid.best_params_)"
    ]
   },
   {
@@ -1671,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 17,
    "id": "ffefa9f2",
    "metadata": {},
    "outputs": [