Science

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

  • Downloads：3004
  • Type：Epub+TxT+PDF+Mobi
  • Create Date：2021-05-27 11:55:21
  • Update Date：2025-09-07
  • Status：finish
  • Author：Trevor Hastie
  • ISBN：0387848576
  • Environment：PC/Android/iPhone/iPad/Kindle
  • Download

Summary

During the past decade there has been an explosion in computation and information technology。 With it have come vast amounts of data in a variety of fields such as medicine， biology， finance， and marketing。 The challenge of understanding these data has led to the development of new tools in the field of statistics， and spawned new areas such as data mining， machine learning， and bioinformatics。 Many of these tools have common underpinnings but are often expressed with different terminology。 This book describes the important ideas in these areas in a common conceptual framework。 While the approach is statistical， the emphasis is on concepts rather than mathematics。 Many examples are given， with a liberal use of color graphics。 It is a valuable resource for statisticians and anyone interested in data mining in science or industry。 The book's coverage is broad， from supervised learning （prediction） to unsupervised learning。 The many topics include neural networks， support vector machines， classification trees and boosting---the first comprehensive treatment of this topic in any book。

This major new edition features many topics not covered in the original， including graphical models， random forests， ensemble methods， least angle regression & path algorithms for the lasso， non-negative matrix factorization， and spectral clustering。 There is also a chapter on methods for ``wide'' data （p bigger than n）， including multiple testing and false discovery rates。

Trevor Hastie， Robert Tibshirani， and Jerome Friedman are professors of statistics at Stanford University。 They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title。 Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces。 Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap。 Friedman is the co-inventor of many data-mining tools including CART， MARS， projection pursuit and gradient boosting。

Download

Reviews

Linquan ，

This book covers most of the topics in statistical learning， but it is written in a way that is too terse。

Daniel Walton ，

This book has been the referential authority for current users of supervised and unsupervised ML。 Having already an econometrics and probability background， this book was quite accessible and enjoyable to read。 I appreciate the methodical and careful style， though at times it feels terse。 I guess the reason is that the book is already quite long and is not meant to be a deep dive into methodology or theory。 That said， the book is very good as an introduction and a reference to ML methods。 I thin This book has been the referential authority for current users of supervised and unsupervised ML。 Having already an econometrics and probability background， this book was quite accessible and enjoyable to read。 I appreciate the methodical and careful style， though at times it feels terse。 I guess the reason is that the book is already quite long and is not meant to be a deep dive into methodology or theory。 That said， the book is very good as an introduction and a reference to ML methods。 I think a semester course using this book should be part of the standard graduate curriculum in economics。 。。。more

Jennifer ，

will read and study this again。

Camellia ，

I love this book。 It’s been my constant fallback last couple of years。 Whenever a question sprung up in my head about the fundamentals of an algorithm， ESL was there with just the precise， succinct information I needed。 I normally don’t write reviews for textbooks， but this one had to be done。 I owe one to ESL。

Andrew ，

Although covering wide range of topics， the book， especially towards the end， reads as a thick overview article， rather than a textbook。 Yes， there're many problems to work on at the end of any chapter， but most concepts， ideas and algorithms presented would require the reader to refer to "original papers" if he attempts to implement them in computer code。 So， while theoretically informative， the book is seriously lacking on practical level。 More of a review than a reference。 Although covering wide range of topics， the book， especially towards the end， reads as a thick overview article， rather than a textbook。 Yes， there're many problems to work on at the end of any chapter， but most concepts， ideas and algorithms presented would require the reader to refer to "original papers" if he attempts to implement them in computer code。 So， while theoretically informative， the book is seriously lacking on practical level。 More of a review than a reference。 。。。more

sarah chang ，

A very comprehensive book on machine learning， but not much content on deep learning。 Still worth a lot to be a reference book as the Bible of machine learning。

Rohit Goswami ，

A more detailed companion piece to the introductory ISLR， this is an excellent introduction。 The only critique would be that， it is too even-handed to influence the mindset of the reader much。

Jack ，

read another book

Sean ，

good reference text。 Selective reading。

Gregory Reshetniak ，

Best book on data science ever。

Irvi ，

Rigorous and mathematically dense books for machine learning。 One of the most challenging books I’ve ever read。

Mlv Prasad ，

This review has been hidden because it contains spoilers。 To view it， click here。 Best stat book for data scientist

Dileep ，

Amazing read for anyone who is interested in Data Science。 The chapters are all very well written。

Miguel Martins ，

A clear and not-so-heavy on the math side introduction to Data Science and Statistical Learning。 I did not finish the book on its entirety since I already was versed in some of the topics。 Notwithstanding， even in such situations， a quick glance gave me more intuition and nuance regarding to what I already knew。I also learned a lot of new concepts， every Data Scientist should read this book。

Dani Mexuto ，

Creo que é o primeiro caso no que o goodreads me axuda coa lectura。 É un libro que comecei en 2016， tiña impreso， coas súas 600 páxinas de fotocopias nun caixón de tela do IKEA coa botas de basket e negro futuro pero vida sen sobresaltos。 E cada vez que entraba aquí pois lembrábame del e acabeino esta semana。 Está moi ben escrito， parécese aos apuntes de María Merlán da xeración do 2005 de Teleco。 Conciso， explica todo， brevemente， sen matemáticas novas， estatística básica， boa coa que chegas a Creo que é o primeiro caso no que o goodreads me axuda coa lectura。 É un libro que comecei en 2016， tiña impreso， coas súas 600 páxinas de fotocopias nun caixón de tela do IKEA coa botas de basket e negro futuro pero vida sen sobresaltos。 E cada vez que entraba aquí pois lembrábame del e acabeino esta semana。 Está moi ben escrito， parécese aos apuntes de María Merlán da xeración do 2005 de Teleco。 Conciso， explica todo， brevemente， sen matemáticas novas， estatística básica， boa coa que chegas a todos os conceptos。 Non se sobresae en ningún punto， é o ceo da xente que traballa a modo e sistemáticamente （ Por exemplo cando explica ADABoost， usa tres liñas de texto para explicalo e con iso chégache para entrar á ilustración da fórmula do erro do algoritmo， SUPER práctico ）A min valeume para entender que non quero adicarme ao Machine Learning。 Non capta a miña atención en absoluto， é estatística con ínfulas， creo que ten bias que van facer encallar a esta técnica en problemas complexos como a validación de algoritmos de condución autónoma。 Creo que é imposíbel discernir a influencia dos datos dentro do proceso de adestramento dun detector。 Un detector de peóns será sempre moi dependente dos peóns nos vídeos é pura teoría de vectores。 E non me interesa， perdinme moita información por lelo sen interese。 Que mágoa， bueno， grazas goodreads。 É ser constante na vida realmente tan útil？ xD 。。。more

Chris ，

it's the classic for good reason， well written and well organized， but this field is not as magical as people believe。 And decorating machine-learning books with informative， colorful， frequent pictures is absolutely what mathematical educators everywhere should be doing， but unfortunately it's only the intellectually vacuous computer fields that ever seem to stick enough pretty pictures in their books。I would like to say machine learning won't make you the money you think it will， but sadly it it's the classic for good reason， well written and well organized， but this field is not as magical as people believe。 And decorating machine-learning books with informative， colorful， frequent pictures is absolutely what mathematical educators everywhere should be doing， but unfortunately it's only the intellectually vacuous computer fields that ever seem to stick enough pretty pictures in their books。I would like to say machine learning won't make you the money you think it will， but sadly it does make people money---just for the wrong reasons。 。。。more

Leonardo ，

Está un poco viejo， pero es el libro fundamental en toda la cuestión del Statical Learning。For a more mathematically intense introduction to the theory of machine learning， see （Hastie， Tibshirani & Friedman， 2009）。 Natural Language Processing with Python Pág。256The math behind kernelized support vector machines is a bit involved， and is beyond the scope of this book。 You can find the details in Chapter 1 of Hastie， Tibshirani， and Friedman’s The Elements of Statistical Learning。 However， we wil Está un poco viejo， pero es el libro fundamental en toda la cuestión del Statical Learning。For a more mathematically intense introduction to the theory of machine learning， see （Hastie， Tibshirani & Friedman， 2009）。 Natural Language Processing with Python Pág。256The math behind kernelized support vector machines is a bit involved， and is beyond the scope of this book。 You can find the details in Chapter 1 of Hastie， Tibshirani， and Friedman’s The Elements of Statistical Learning。 However， we will try to give you some sense of the idea behind the method。 Introduction to Machine Learning with Python Pág。92 。。。more

Terran M ，

Note that somehow the Kindle Edition is not associated with all the other editions of this book in the GoodReads database。 See the rest of them at Elements of Statistical Learning Note that somehow the Kindle Edition is not associated with all the other editions of this book in the GoodReads database。 See the rest of them at Elements of Statistical Learning 。。。more

Amit Misra ，

I read this book for work， during work， but I'm falling behind my yearly goal so I'm including it on goodreads :PThis book has a lot in it， and is incredibly dense。 However， it's well worth it。 It contains not quite everything about statistics and machine learning that someone needs to know to do data science， but it comes close。 The drawback is that this book is hard to understand。 You need to know a lot， or be willing to learn a lot from other resources， to actually get a lot from this book。 E I read this book for work， during work， but I'm falling behind my yearly goal so I'm including it on goodreads :PThis book has a lot in it， and is incredibly dense。 However， it's well worth it。 It contains not quite everything about statistics and machine learning that someone needs to know to do data science， but it comes close。 The drawback is that this book is hard to understand。 You need to know a lot， or be willing to learn a lot from other resources， to actually get a lot from this book。 Even as someone with a good stats and ML background， there were some parts where I had to find online sources to get explanations of even how to start thinking about what's in the book。Now that I've gone through it once， I know I'll be going back to this time and time again since it is such a good resource。 I also plan on going back and re-reading at least some of the chapters as necessary。 。。。more

Jin Shusong ，

Everyone in machine learning area should read it。

Dan Boeriu ，

For the mathematician - this book is too terse and hard to learn from to the point of pretentiousness。For the software engineer - the algorithms presentation in this book is poor。 A bunch of phrases with no clear state change， step computations， etc。In general - a lot of pompous presentations and hand waiving material。Something positive: the paper is top quality。

Wojtekwalczak ，

Nice as a reference or an overview， but not necessarily as a source for learning。 So many approaches and techniques are described in this book， that out of necessity， their description is very general， very condensed and very mathematical。

Jesus Angulo ，

Starting the journey！！

Razvan Coca ，

It sounds like the right perspective on Machine Leaning

Ilknur ，

This book can be downloaded freely from the authors' web page。 This book can be downloaded freely from the authors' web page。 。。。more

Abhilash Kulkarni ，

The best book for an in depth understanding of pattern recognition and statistical learning。

Kirill ，

Well， it was one of the most channeling books I've read in my career。 It is a rigorous and mathematically dense book on machine learning techniques。Be sure to refine your understanding of linear algebra and convex optimization before reading this book。 Nonetheless， the investment will totally worth it。 Well， it was one of the most channeling books I've read in my career。 It is a rigorous and mathematically dense book on machine learning techniques。Be sure to refine your understanding of linear algebra and convex optimization before reading this book。 Nonetheless， the investment will totally worth it。 。。。more

Reference

google book https://www.google.com/search?tbm=bks&q=The Elements of Statistical Learning: Data Mining, Inference, and Prediction

hathitrust digital library https://babel.hathitrust.org/cgi/ls?q1=The Elements of Statistical Learning: Data Mining, Inference, and Prediction&field1=ocr&a=srchls&ft=ft&lmt=ft

The British Library https://bll01.primo.exlibrisgroup.com/discovery/search?query=any,contains,The Elements of Statistical Learning: Data Mining, Inference, and Prediction&tab=LibraryCatalog&search_scope=Not_BL_Suppress&vid=44BL_INST:BLL01&lang=en&offset=0