| 期刊全稱 | An Introduction to Statistical Learning | | 期刊簡稱 | with Applications in | | 影響因子2023 | Gareth James,Daniela Witten,Robert Tibshirani | | 視頻video | http://file.papertrans.cn/156/155492/155492.mp4 | | 發(fā)行地址 | Presents an essential statistical learning toolkit for practitioners in science, industry, and other fields.Demonstrates application of the statistical learning methods in R.Includes new chapters on d | | 學(xué)科分類 | Springer Texts in Statistics | | 圖書封面 |  | | 影響因子 | .An Introduction to Statistical Learning. provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform..Two of the authors co-wrote .The Elements of Statistical Learning. (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning | | Pindex | Textbook 2021Latest edition |
The information of publication is updating
|
|
|Archiver|手機版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-31 03:44
發(fā)表于 2025-3-21 17:18:52
