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@@ -60,14 +60,23 @@ This partitioning allows you to explore the training data, occassionally generat
### Other references
-More so than any other part of the book, these chapters are opinionated, and I'm not aware of any other presentation of exploratory model analysis.
+The modelling chapters are even more opinionated than the rest of the book. I approach modelling from a somewhat different perspective to most others, and there is relatively little space devoted to it. Modelling really deserves a book on its own, so I'd highly recommend that you read at least one of these three books:
-* Regression modelling strategies by Frank Harrell.
+* *Statistical Modeling: A Fresh Approach* by Danny Kaplan,
+ . This book provides
+ a gentle introduction to modelling, where you build your intuition,
+ mathematical tools, and R skills in parallel.
-* *Statistical Modeling: A Fresh Approach* by Danny Kaplan;
+* *An Introduction to Statistical Learning* by Gareth James, Daniela Witten,
+ Trevor Hastie, and Robert Tibshirani,
+ (available online for free). This book presents a family of modern modelling
+ techniques collectively known as statistical learning.
-* *An Introduction to Statistical Learning* by James, Witten, Hastie, and Tibshirani
-
-* *Applied Predictive Modeling* by Kuhn and Johnson.
+* *Applied Predictive Modeling* by Max Kuhn and Kjell Johnson,
+ . This book is a companion to the
+ __caret__ package, and provides practical tools for dealing with real-life
+ predictive modelling challenges.
+