Custom cover image
Custom cover image

Statistical Learning from a Regression Perspective / by Richard A. Berk

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer Texts in Statistics | Springer eBook CollectionPublisher: Cham : Springer International Publishing, 2020Publisher: Cham : Imprint: Springer, 2020Edition: 3rd ed. 2020Description: 1 Online-Ressource(XXVI, 433 p. 143 illus., 107 illus. in color.)ISBN:
  • 9783030401894
Subject(s): Additional physical formats: 9783030401887 | 9783030401900 | 9783030429232 | Erscheint auch als: Statistical learning from a regression perspective. Druck-Ausgabe Third edition. Cham, Switzerland : Springer, 2020. xxvi, 433 Seiten | Erscheint auch als: 9783030401900 Druck-Ausgabe | Erscheint auch als: 9783030429232 Druck-AusgabeRVK: RVK: SK 840 | QH 234DOI: DOI: 10.1007/978-3-030-40189-4Online resources: Summary: 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. .Summary: 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.PPN: PPN: 1724272683Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS
No physical items for this record

Powered by Koha