From e9d67ce3894ce8813d0e507ba8e314bd700d9fd1 Mon Sep 17 00:00:00 2001 From: hadley Date: Thu, 21 Jul 2016 07:48:16 -0500 Subject: [PATCH] More on the modelling books --- model.Rmd | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/model.Rmd b/model.Rmd index af1fb7f..27e1f57 100644 --- a/model.Rmd +++ b/model.Rmd @@ -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. +