Statistical Learning from a Regression Perspective. 3rd ed. 2020

種類:

電子ブック

責任表示:

by Richard A. Berk

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2020

著者名:

• Berk, Richard A.
• SpringerLink (Online service)

シリーズ名:

Springer Texts in Statistics ;

ISBN:

9783030401894 [3030401898]    

注記:

Preface -- Preface To Second Edition -- Preface To Third Edition -- 1 Statistical Learning as a Regression Problem -- 2 Splines, Smoothers, and Kernels -- 3 Classification and Regression Trees (CART) -- 4 Bagging -- 5 Random Forests -- 6 Boosting -- 7 Support Vector Machines -- 8 Neural Networks -- 9 Reinforcement Learning and Genetic Algorithms -- 10 Integration Themes and a Bit of Craft Lore -- Index. .
This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advanc

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