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Statistical Learning from a Regression Perspective

  • Textbook
  • © 2020

Overview

Authors:
  1. Richard A. Berk

    1. Department of Criminology, Schools of Arts and Sciences, University of Pennsylvania, Philadelphia, USA

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  • 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

Part of the book series: Springer Texts in Statistics (STS)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xxvi
    Download chapter PDF

  2. Statistical Learning as a Regression Problem

    • Richard A. Berk
    Pages 1-72

  3. Splines, Smoothers, and Kernels

    • Richard A. Berk
    Pages 73-156

  4. Classification and Regression Trees (CART)

    • Richard A. Berk
    Pages 157-211

  5. Bagging

    • Richard A. Berk
    Pages 213-232

  6. Random Forests

    • Richard A. Berk
    Pages 233-295

  7. Boosting

    • Richard A. Berk
    Pages 297-337

  8. Support Vector Machines

    • Richard A. Berk
    Pages 339-359

  9. Neural Networks

    • Richard A. Berk
    Pages 361-399

  10. Reinforcement Learning and Genetic Algorithms

    • Richard A. Berk
    Pages 401-413

  11. Integrating Themes and a Bit of Craft Lore

    • Richard A. Berk
    Pages 415-425

  12. Back Matter

    Pages 427-433
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Keywords

About this book

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 advances in recent years, among which are:

  • the development of overarching, conceptual frameworks for statistical learning;
  • the impact of  “big data” on statistical learning;
  • the nature and consequences of post-model selection statistical inference;
  • deep learning in various forms;
  • the special challenges to statistical inference posed by statistical learning;
  • the fundamental connections between data collection and data analysis;
  • interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy.

This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.

Reviews

“It could readily be a textbook for an applications-focused course at the graduate level as each chapter comes with exercises … . Examples with accompanying code also appear throughout the chapters which provide a scaffold for getting started … . Berk’s pragmatic advice will serve a wide audience from practitioners to educators to students.” (Sara Stoudt, MAA Reviews, December 12, 2021)

Authors and Affiliations

  • Department of Criminology, Schools of Arts and Sciences, University of Pennsylvania, Philadelphia, USA

    Richard A. Berk

About the author

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.

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