LEADER 05583nam 22007215 450 001 996418276503316 005 20200701225655.0 010 $a3-030-40189-8 024 7 $a10.1007/978-3-030-40189-4 035 $a(CKB)4100000011325777 035 $a(MiAaPQ)EBC6246037 035 $a(DE-He213)978-3-030-40189-4 035 $a(PPN)248602160 035 $a(EXLCZ)994100000011325777 100 $a20200629d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Learning from a Regression Perspective$b[electronic resource] /$fby Richard A. Berk 205 $a3rd ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (xxvi, 433 pages) $cillustrations 225 1 $aSpringer Texts in Statistics,$x1431-875X 311 $a3-030-40188-X 320 $aIncludes bibliographical references and index. 327 $aPreface -- 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. . 330 $aThis 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. 410 0$aSpringer Texts in Statistics,$x1431-875X 606 $aStatistics  606 $aProbabilities 606 $aPublic health 606 $aPsychology?Methodology 606 $aPsychological measurement 606 $aSocial sciences 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aPublic Health$3https://scigraph.springernature.com/ontologies/product-market-codes/H27002 606 $aPsychological Methods/Evaluation$3https://scigraph.springernature.com/ontologies/product-market-codes/Y20040 606 $aMethodology of the Social Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/X17000 615 0$aStatistics . 615 0$aProbabilities. 615 0$aPublic health. 615 0$aPsychology?Methodology. 615 0$aPsychological measurement. 615 0$aSocial sciences. 615 14$aStatistical Theory and Methods. 615 24$aProbability Theory and Stochastic Processes. 615 24$aStatistics for Social Sciences, Humanities, Law. 615 24$aPublic Health. 615 24$aPsychological Methods/Evaluation. 615 24$aMethodology of the Social Sciences. 676 $a519.5 700 $aBerk$b Richard A$4aut$4http://id.loc.gov/vocabulary/relators/aut$0558720 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418276503316 996 $aStatistical learning from a regression perspective$91523646 997 $aUNISA