From 82f3d2d80baf3591ee0c1ae0749ce268fc3e1e0e Mon Sep 17 00:00:00 2001 From: George Nick Gorzynski Date: Wed, 7 Feb 2024 15:28:35 +0000 Subject: [PATCH] Fix Link Link for Chapter 4 was malformed with no closing ']' bracket. --- Chapter_2_Background/MachineLearningForFraudDetection.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Chapter_2_Background/MachineLearningForFraudDetection.md b/Chapter_2_Background/MachineLearningForFraudDetection.md index c011bc3..ebbb8ae 100644 --- a/Chapter_2_Background/MachineLearningForFraudDetection.md +++ b/Chapter_2_Background/MachineLearningForFraudDetection.md @@ -86,7 +86,7 @@ y,\hat{y} &= & $$ ```{note} -The zero/one loss function is a standard loss function for binary classification problems. It is however not well suited for credit card fraud detection problems, due to the high-class imbalance (much more genuine than fraudulent transactions). Estimating the performance of a fraud detection system is a non-trivial issue, which will be covered in depth in [Chapter 4[(Performance_Metrics). +The zero/one loss function is a standard loss function for binary classification problems. It is however not well suited for credit card fraud detection problems, due to the high-class imbalance (much more genuine than fraudulent transactions). Estimating the performance of a fraud detection system is a non-trivial issue, which will be covered in depth in [Chapter 4](Performance_Metrics). ``` To obtain a fair estimate of a prediction model performance, an important methodological practice, known as *validation*, is to evaluate the performance of a prediction model on data that were not used for training. This is achieved by splitting the dataset, before training, into a *training set* and a *validation set*. The training set is used for the training of the prediction model (that is, to find the parameters $\theta$ that minimize the loss on the training set). Once the parameters $\theta$ have been fixed, the loss is estimated with the validation set, which gives a better estimate of the performance that the prediction model is expected to have on future (and unseen) transactions.