Skip to content

Elements of Statistical Learning Data Mining, Inference, and Prediction

Spend $50 to get a free DVD!

ISBN-10: 0387848576

ISBN-13: 9780387848570

Edition: 2nd 2009

Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman, J. H. Friedman

List price: $89.95
Shipping box This item qualifies for FREE shipping.
Blue ribbon 30 day, 100% satisfaction guarantee!
what's this?
Rush Rewards U
Members Receive:
Carrot Coin icon
XP icon
You have reached 400 XP and carrot coins. That is the daily max!

Customers also bought

Book details

List price: $89.95
Edition: 2nd
Copyright year: 2009
Publisher: Springer
Publication date: 4/21/2017
Binding: Hardcover
Pages: 745
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 3.234
Language: English

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Introduction
Overview of supervised learning
Linear methods for regression
Linear methods for classification
Basis expansions and regularization
Kernel smoothing methods
Model assessment and selection
Model inference and averaging
Additive models, trees, and related methods
Boosting and additive trees
Neural networks
Support vector machines and flexible discriminants
Prototype methods and nearest-neighbors
Unsupervised learning