| 书目名称 | Statistical Learning from a Regression Perspective | | 编辑 | Richard A. Berk | | 视频video | | | 概述 | Accessible discussion of statistical learning procedures for practitioners with real-world applications in the social and policy sciences.Methods also of interest in the natural sciences and engineeri | | 丛书名称 | Springer Texts in Statistics | | 图书封面 |  | | 描述 | 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. .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. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. .The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. All of the analyses included are done in R with code | | 出版日期 | Textbook 20162nd edition | | 关键词 | classification; random forests; statistical learning; support vector machines; regression analysis; stati | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-319-44048-4 | | isbn_softcover | 978-3-319-82969-2 | | isbn_ebook | 978-3-319-44048-4Series ISSN 1431-875X Series E-ISSN 2197-4136 | | issn_series | 1431-875X | | copyright | Springer Nature Switzerland AG 2016 |
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发表于 2025-3-21 17:30:20
