This book 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.

Provides accompanying, fully updated R code Evaluates the ethical and political implications of the application of algorithmic methods Features a new chapter on deep learning

Autorentext

Richard Berk is Distinguished Professor of Statistics Emeritus at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of statistical applications in the social and natural sciences.

Inhalt
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.