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More on the modelling books

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hadley 2016-07-21 07:48:16 -05:00
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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.
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