[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160263-en":3,"doc-seo-160263-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},160263,2336475104362,"Mali","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",6,"Technology","An Introduction to Statistical Learning - with Applications in R","Statistical learning presents tools for modeling and understanding complex datasets, drawing together advances in statistics, computer science, and machine learning. The book addresses core methods including sparse regression via lasso, classification and regression trees, boosting, and support vector machines, framed for modern “Big Data” applications across research, marketing, and finance. It emphasizes practical implementation through hands-on labs using the R statistical software, targeting advanced undergraduates, master’s students, and practitioners who need a broader, less technical introduction centered on applications.","Springer Texts in Statistics  \nGareth James Daniela Witten Trevor Hastie Robert Tibshirani  \nAn Introduction to Statistical Learning  \nwith Applications in R  \nSpringer Texts in Statistics 103  \nSeries Editors:  \nG. Casella  \nS. Fienberg  \nI. Olkin  \nFor further volumes:  \n[http://www.springer.com/series/417](http://www.springer.com/series/417)  \nGareth James • Daniela Witten • Trevor Hastie Robert Tibshirani  \nAn Introduction to Statistical Learning  \nwith Applications in R  \n1 3  \nGareth James  \nDepartment of Information and Operations Management University of Southern California  \nLos Angeles, CA, USA  \nTrevor Hastie Department of Statistics Stanford University Stanford, CA, USA  \nDaniela Witten Department of Biostatistics University of Washington Seattle, WA, USA  \nRobert Tibshirani Department of Statistics Stanford University Stanford, CA, USA  \nISSN 1431-875X  \nISBN 978-1-4614-7137-0 ISBN 978-1-4614-7138-7 (eBook)  \nDOI 10.1007/978-1-4614-7138-7  \nSpringer New York Heidelberg Dordrecht London Library of Congress Control Number: 2013936251  \n© Springer Science+Business Media New York 2013  \nThis work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law.  \nThe use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.  \nWhile the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.  \nPrinted on acid-free paper  \nSpringer is part of Springer Science+Business Media ([www.springer.com](www.springer.com))  \nTo our parents:  \nAlison and Michael James Chiara Nappi and Edward Witten Valerie and Patrick Hastie Vera and Sami Tibshirani  \nand to our families:  \nMichael, Daniel, and Catherine Ari  \nSamantha, Timothy, and Lynda Charlie, Ryan, Julie, and Cheryl  \nPreface  \nStatistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. The field encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines.  \nWith the explosion of “Big Data” problems, statistical learning has become a very hot field in many scientific areas as well as marketing, finance, and other business disciplines. People with statistical learning skills are in high demand.  \nOne of the first books in this area—The Elements of Statistical Learning (ESL) (Hastie, Tibshirani, and Friedman)—was published in 2001, ","cbCaik8TaLUboRZv","https://ap.wps.com/l/cbCaik8TaLUboRZv","pdf",13342034,1,441,"English","en",105,"# Preface\n# Contents\n## 1 Introduction\n## 2 Statistical Learning\n## 2.1 What Is Statistical Learning?\n## 2.1.1 Why Estimate f?\n## 2.1.2 How Do We Estimate f?\n## 2.1.3 The Trade-Off Between Prediction Accuracy and Model Interpretability\n## 2.1.4 Supervised Versus Unsupervised Learning\n## 2.1.5 Regression Versus C","[{\"question\":\"What does statistical learning focus on in this book?\",\"answer\":\"It focuses on tools for modeling and understanding complex datasets, connecting statistical methods with developments in computer science and machine learning.\"},{\"question\":\"Which topics and methods are covered?\",\"answer\":\"The content includes sparse regression using the lasso, classification and regression trees, boosting, and support vector machines, along with related supervised learning concepts.\"},{\"question\":\"Who is this book suitable for and how are R labs used?\",\"answer\":\"It suits advanced undergraduates, master’s students, and others in quantitative or related fields. Labs show how to implement each statistical learning method using the R software for hands-on experience.\"}]","An Introduction to Statistical Learning - with Applications in R | PDF",1788052996,1111,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-introduction-to-statistical-learning-with-applications-in-r","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-introduction-to-statistical-learning-with-applications-in-r/160263/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-30",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does statistical learning focus on in this book?","Question",{"text":75,"@type":76},"It focuses on tools for modeling and understanding complex datasets, connecting statistical methods with developments in computer science and machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which topics and methods are covered?",{"text":80,"@type":76},"The content includes sparse regression using the lasso, classification and regression trees, boosting, and support vector machines, along with related supervised learning concepts.",{"name":82,"@type":73,"acceptedAnswer":83},"Who is this book suitable for and how are R labs used?",{"text":84,"@type":76},"It suits advanced undergraduates, master’s students, and others in quantitative or related fields. 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