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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,
+  <http://www.mosaic-web.org/go/StatisticalModeling/>. 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, <http://www-bcf.usc.edu/~gareth/ISL/> 
+  (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, 
+  <http://appliedpredictivemodeling.com>. This book is a companion to the 
+  __caret__ package, and provides practical tools for dealing with real-life
+  predictive modelling challenges.
 
+<!-- 
 For much of the insirpiration of the visualisations of these models, and extensions to more complex families, you might like "MODEL VIS PAPER"
+-->