Statistical Learning from a Regression Perspective. 2nd ed. 2016

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, 2016

著者名:

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

シリーズ名:

Springer Texts in Statistics ;

ISBN:

9783319440484 [3319440489]    

注記:

Statistical Learning as a Regression Problem -- Splines, Smoothers, and Kernels -- Classification and Regression Trees (CART) -- Bagging -- Random Forests -- Boosting -- Support Vector Machines -- Some Other Procedures Briefly -- Broader Implications and a Bit of Craft Lore.
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea

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