From 77498cf0dc37f48264ecf84c951ad0e8fd168873 Mon Sep 17 00:00:00 2001
From: David Dalpiaz <9003346+daviddalpiaz@users.noreply.github.com>
Date: Mon, 18 Nov 2024 09:04:51 -0600
Subject: [PATCH] fix broken link

---
 logistic.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/logistic.Rmd b/logistic.Rmd
index b39adf4..bf0f6f3 100644
--- a/logistic.Rmd
+++ b/logistic.Rmd
@@ -1113,7 +1113,7 @@ get_spec(conf_mat_90)
 
 While this is far fewer false positives, is it acceptable though? Still probably not. Also, don't forget, this would actually be a terrible spam detector today since this is based on data from a very different era of the internet, for a very specific set of people. Spam has changed a lot since the 90s! (Ironically, machine learning is probably partially to blame.)
 
-This chapter has provided a rather quick introduction to classification, and thus, machine learning. For a more complete coverage of machine learning, [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/){target="_blank"} is a highly recommended resource. Additionally, [`R` for Statistical Learning](https://daviddalpiaz.github.io/r4sl/){target="_blank"} has been written as a supplement which provides additional detail on how to perform these methods using `R`. The [classification](https://daviddalpiaz.github.io/r4sl/classification-overview.html){target="_blank"} and [logistic regression](https://daviddalpiaz.github.io/r4sl/logistic-regression.html){target="_blank"} chapters might be useful.
+This chapter has provided a rather quick introduction to classification, and thus, machine learning. For a more complete coverage of machine learning, [An Introduction to Statistical Learning](https://www.statlearning.com/){target="_blank"} is a highly recommended resource. Additionally, [`R` for Statistical Learning](https://daviddalpiaz.github.io/r4sl/){target="_blank"} has been written as a supplement which provides additional detail on how to perform these methods using `R`. The [classification](https://daviddalpiaz.github.io/r4sl/classification-overview.html){target="_blank"} and [logistic regression](https://daviddalpiaz.github.io/r4sl/logistic-regression.html){target="_blank"} chapters might be useful.
 
 We should note that the code to perform classification using logistic regression is presented in a way that illustrates the concepts to the reader. In practice, you may prefer to use a more general machine learning pipeline such as [`caret`](http://topepo.github.io/caret/index.html){target="_blank"} in `R`. This will streamline processes for creating predictions and generating evaluation metrics.