Statistical Learning from a Regression Perspective

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Accessible discussion of statistical learning procedures for practitioners Lots of real applications discussed Intuitive explanations and visual representation of underlying statistical concepts

Autorentext

Richard Berk is Distinguished Professor of Statistics Emeritus from the Department of Statistics 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 applications in the social and natural sciences.

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Inhalt
Statistical Learning as a Regression Problem.- Regression Splines and Regression Smoothers.- Classification and Regression Trees (CART).- Bagging.- Random Forests.- Boosting.- Support Vector Machines.- Broader Implications and a Bit of Craft Lore.