[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-46174-en":3,"doc-seo-46174-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},46174,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The Elements of Statistical Learning Trevor Hastie Robert Tibshirani","The Elements of Statistical Learning presents a structured, theory-to-practice treatment of data mining, statistical inference, and predictive modeling. The second edition expands the book with four new chapters and refreshes existing material while preserving the first edition’s familiar organization. The preface outlines key updates across supervised learning, regression and classification, regularization, kernel smoothing, assessment via cross-validation, inference and averaging, trees and boosting, neural networks, support vector methods, prototype and nearest-neighbor ideas, and a broad set of unsupervised techniques for dimensionality reduction, clustering, matrix factorization, and high-dimensional “p ≫ N” problems.","Springer Series in Statistics  \nTrevor Hastie Robert Tibshirani Jerome Friedman  \nThe Elements of Statistical Learning  \nData Mining, Inference, and Prediction  \nSecond Edition  \nThis is page v  \nPrinter: Opaque this  \nTo our parents:  \nValerie and Patrick Hastie Vera and Sami Tibshirani Florence and Harry Friedman  \nand to our families:  \nSamantha, Timothy, and Lynda Charlie, Ryan, Julie, and Cheryl Melanie, Dora, Monika, and Ildiko  \nvi  \nThis is page vii  \nPrinter: Opaque this  \nPreface to the Second Edition  \nIn God we trust, all others bring data.  \n–William Edwards Deming (1900-1993)1  \nWe have been gratified by the popularity of the first edition of The Elements of Statistical Learning. This, along with the fast pace of research in the statistical learning field, motivated us to update our book with a second edition.  \nWe have added four new chapters and updated some of the existing chapters. Because many readers are familiar with the layout of the first edition, we have tried to change it as little as possible. Here is a summary of the main changes:  \n1 On the Web, this quote has been widely attributed to both Deming and Robert W. Hayden; however Professor Hayden told us that he can claim no credit for this quote, and ironically we could find no “data” confirming that Deming actually said this.  \nviii Preface to the Second Edition  \nChapter What’s new  \n\n| 1. Introduction |  |\n| --- | --- |\n| 2. Overview of Supervised Learning |  |\n| 3. Linear Methods for Regression | LAR algorithm and generalizations of the lasso |\n| 4. Linear Methods for Classification | Lasso path for logistic regression |\n| 5. Basis Expansions and Regularization | Additional illustrations of RKHS |\n| 6. Kernel Smoothing Methods |  |\n| 7. Model Assessment and Selection | Strengths and pitfalls of crossvalidation |\n| 8. Model Inference and Averaging |  |\n| 9. Additive Models, Trees, and Related Methods |  |\n| 10. Boosting and Additive Trees | New example from ecology; some material split off to Chapter 16 . |\n| 11. Neural Networks | Bayesian neural nets and the NIPS 2003 challenge |\n| 12. Support Vector Machines and Flexible Discriminants | Path algorithm for SVM classifier |\n| 13. Prototype Methods and Nearest-Neighbors |  |\n| 14. Unsupervised Learning | Spectral clustering, kernel PCA, sparse PCA, non-negative matrix factorization archetypal analysis, nonlinear dimension reduction, Google page rank algorithm, a direct approach to ICA |\n| 15. Random Forests | New |\n| 16. Ensemble Learning | New |\n| 17. Undirected Graphical Models | New |\n| 18. High-Dimensional Problems | New |\n\nSome further notes:  \n• Our first edition was unfriendly to colorblind readers; in particular, we tended to favor red/green contrasts which are particularly troublesome. We have changed the color palette in this edition to a large extent, replacing the above with an orange/blue contrast.  \n• We have changed the name of Chapter 6 from “Kernel Methods” to“Kernel Smoothing Methods”, to avoid confusion with the machinelearning kernel method that is discussed in the context of support vector machines (Chapter 11) and more generally in Chapters 5 and 14 .  \n• In the first edition, the discussion of error-rate estimation in Chapter 7 was sloppy, as we did not clearly differentiate the notions of conditional error rates (conditional on the training set) and unconditional rates. We have fixed this in the new edition.  \nPreface to the Second Edition ix  \n• Chapters 15 and 16 follow naturally from Chapter 10, and the chapters are probably best read in that order.  \n• In Chapter 17, we have not attempted a comprehensive treatment of graphical models, and discuss only undirected models and some new methods for their estimation. Due to a lack of space, we have specifically omitted coverage of directed graphical models.  \n• Chapter 18 explores the “p ≫ N” problem, which is learning in highdimensional feature spaces. These problems arise in many areas, including genomic and proteomic studies, and document","cbCaibGorKlLxcxU","https://ap.wps.com/l/cbCaibGorKlLxcxU","pdf",21644344,3,1,764,"English","en",105,"# Preface to the Second Edition\n## Chapter What’s new\n## Some further notes\n# Preface to the First Edition\n## Learning from data","[{\"question\":\"What motivated the update from the first edition to the second edition?\",\"answer\":\"Growing popularity of the first edition and the fast pace of research in statistical learning motivated updating the book for a second edition.\"},{\"question\":\"What are the major changes introduced in the second edition?\",\"answer\":\"Four new chapters were added and parts of existing chapters were updated, with the layout changed as little as possible to match the original organization.\"},{\"question\":\"Which key topics are highlighted across the book’s chapters?\",\"answer\":\"The chapter overview covers supervised learning, regression and classification methods (including regularization), kernel smoothing, model assessment and inference, tree and boosting methods, neural networks, support vector machines, prototype/nearest-neighbors, unsupervised learning, random forests, ensemble learning, graphical models, and high-dimensional learning problems.\"}]",1783472318,1925,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-elements-of-statistical-learning-trevor-hastie-robert-tibshirani","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"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":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/the-elements-of-statistical-learning-trevor-hastie-robert-tibshirani/46174/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-12","2026-07-08",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What motivated the update from the first edition to the second edition?","Question",{"text":75,"@type":76},"Growing popularity of the first edition and the fast pace of research in statistical learning motivated updating the book for a second edition.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the major changes introduced in the second edition?",{"text":80,"@type":76},"Four new chapters were added and parts of existing chapters were updated, with the layout changed as little as possible to match the original organization.",{"name":82,"@type":73,"acceptedAnswer":83},"Which key topics are highlighted across the book’s chapters?",{"text":84,"@type":76},"The chapter overview covers supervised learning, regression and classification methods (including regularization), kernel smoothing, model assessment and inference, tree and boosting methods, neural networks, support vector machines, prototype/nearest-neighbors, unsupervised learning, random forests, ensemble learning, graphical models, and high-dimensional learning problems.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]