It turns out that R2 will always increase when more variables are added to the model, even if those variables are only weakly associated with the response. (查看原文)
Chapter 2 introduces the basic terminology and concepts behind statistical
learning.
Chapters 3 and 4 cover classical linear methods for regression and classification.
In particular, Chapter 3 reviews linear regression, the fundamental starting
point for all regression methods. In Chapter 4 we discuss two of the
most important classical classification methods, logistic regression and lin-
ear discriminant analysis.
A central problem in all statistical learning situations involves choosing
the best method for a given application. Hence, in Chapter 5 we introduce
cross-validation and the bootstrap, which can be used to estimate the
accuracy of a number of different methods in order to choose the best one.
In Chapter 6 we consider a host of linear methods, both
classical and more modern, ... (查看原文)
In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details. We have created labs illustrating how to implement each of the statictical learning methods using the popular statistical software package R.
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It's tough to make predictions, especially about the future. (查看原文)
