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‎img/clustering/c1.png

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‎lectures/08-learning/08-learning.Rmd

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```{r,echo = FALSE,out.width = 600}
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knitr::include_graphics("../../img/ISLR/chapter2/2.3.png")
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knitr::include_graphics("imgs/2.3.png")
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```
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```{r,echo = FALSE,out.width = 600}
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knitr::include_graphics("../../img/ISLR/chapter2/2.4.png")
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knitr::include_graphics("imgs/2.4.png")
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```
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```{r,echo = FALSE,out.width = 600}
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knitr::include_graphics("../../img/ISLR/chapter2/2.5.png")
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knitr::include_graphics("imgs/2.5.png")
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```
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```{r,echo = FALSE,out.width = 600}
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knitr::include_graphics("../../img/ISLR/chapter2/2.6.png")
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knitr::include_graphics("imgs/2.6.png")
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```
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## Smooth, not wiggly
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```{r,echo = FALSE,out.width = 600}
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knitr::include_graphics("../../img/ISLR/chapter2/2.5.png")
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knitr::include_graphics("imgs/2.5.png")
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```
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.pull-right[
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## Smooth but high variance (wiggly!)
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```{r,echo = FALSE,out.width = 600}
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knitr::include_graphics("../../img/ISLR/chapter2/2.6.png")
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knitr::include_graphics("imgs/2.6.png")
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```
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```{r,echo = FALSE,out.width = 600}
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knitr::include_graphics("../../img/ISLR/chapter2/2.6.png")
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knitr::include_graphics("imgs/2.6.png")
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knitr::include_graphics("../../img/ISLR/chapter2/2.7.png")
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knitr::include_graphics("imgs/2.7.png")
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knitr::include_graphics("../../img/ISLR/chapter2/2.8.png")
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knitr::include_graphics("imgs/2.8.png")
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```{r,echo = FALSE,out.width = 900}
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knitr::include_graphics("../../img/ISLR/chapter2/2.9.png")
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knitr::include_graphics("imgs/2.9.png")
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* Let's look at our [app online](https://floswald.shinyapps.io/bias_variance/) or `ScPoApps::launchApp("bias_variance_tradeoff")`
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```{r,echo = FALSE}
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knitr::include_graphics("../../img/ISLR/chapter2/app.gif")
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knitr::include_graphics("imgs/app.gif")
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knitr::include_graphics("imgs/2.10.png")
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knitr::include_graphics("../../img/ISLR/chapter2/2.11.png")
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knitr::include_graphics("imgs/2.11.png")
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knitr::include_graphics("../../img/ISLR/chapter2/2.12.png")
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knitr::include_graphics("imgs/2.12.png")
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‎lectures/08-learning/08-learning.html

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class: center, middle, inverse, title-slide
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# ScPoEconometrics: Advanced
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## Statistical Learning
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## Intro to Statistical Learning
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### Florian Oswald
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### SciencesPo Paris </br> 2020-11-24
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### SciencesPo Paris </br> 2020-12-08
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---
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# An Example of `\(f\)`
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# A Parametric Model for `\(f\)`
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# A Non-Parametric Model for `\(f\)`
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&lt;img src="../../img/ISLR/chapter2/2.5.png" width="600" style="display: block; margin: auto;" /&gt;
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# Overfitting: Choosing *Smoothness*
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&lt;img src="../../img/ISLR/chapter2/2.6.png" width="600" style="display: block; margin: auto;" /&gt;
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## Smooth, not wiggly
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&lt;img src="../../img/ISLR/chapter2/2.5.png" width="600" style="display: block; margin: auto;" /&gt;
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&lt;img src="imgs/2.5.png" width="600" style="display: block; margin: auto;" /&gt;
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## Smooth but high variance (wiggly!)
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&lt;img src="../../img/ISLR/chapter2/2.6.png" width="600" style="display: block; margin: auto;" /&gt;
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&lt;img src="imgs/2.6.png" width="600" style="display: block; margin: auto;" /&gt;
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* Let's look at our [app online](https://floswald.shinyapps.io/bias_variance/) or `ScPoApps::launchApp("bias_variance_tradeoff")`
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* Precise Tradeoff depends on `\(f\)`'s shape.
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* Bias declines with flexibility.
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