The Elements of Statiscal Learning
About
About
1 Introduction
2 Overview of Supervised Learning
3 Linear Methods for Regression
4 Linear Methods for Classification
5 Basis Expansions and Regularization
6 Kernel Smoothing Methods
7 Model Assessment and Selection
8 Model Inference and Averaging
9 Additive Models, Trees, and Related Methods
10 Boosting and Additive Trees
11 Neural Networks
12 Support Vector Machines and Flexible Discriminants
13 Prototype Methods and Nearest-neighbors
14 Unsupervised Learning
15 Random Forests
16 Ensemble Learning
17 Undirected Graphical Models
18 High-Dimensional Problems: p » N
References
Table of contents
About
The Elements of Statiscal Learning
Published
January 1, 2026
About
The Elements of Statistical Learning
1 Introduction
