An Introduction to Statistical Learning with Applications in R
Afbeeldingen
Artikel vergelijken
- Engels
- Paperback
- 9781071614204
- 30 juli 2022
- 607 pagina's
Gareth James
(Bron: Wikipedia. Beschikbaar onder de licentie Creative Commons Naamsvermelding/Gelijk delen.)"
Samenvatting
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 researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Productspecificaties
Inhoud
- Taal
- en
- Bindwijze
- Paperback
- Oorspronkelijke releasedatum
- 30 juli 2022
- Aantal pagina's
- 607
- Illustraties
- Nee
Betrokkenen
- Hoofdauteur
- Gareth James
- Tweede Auteur
- Daniela Witten
- Co Auteur
- Daniela Witten
- Hoofduitgeverij
- Springer-Verlag New York Inc.
Overige kenmerken
- Editie
- 2
- Product breedte
- 155 mm
- Product lengte
- 235 mm
- Studieboek
- Ja
- Verpakking breedte
- 155 mm
- Verpakking hoogte
- 39 mm
- Verpakking lengte
- 237 mm
- Verpakkingsgewicht
- 1192 g
EAN
- EAN
- 9781071614204
Je vindt dit artikel in
- Categorieën
- Taal
- Engels
- Beschikbaarheid
- Leverbaar
- Boek, ebook of luisterboek?
- Boek
- Studieboek of algemeen
- Studieboeken
Kies gewenste uitvoering
Prijsinformatie en bestellen
De prijs van dit product is 64 euro en 19 cent.- Prijs inclusief verzendkosten, verstuurd door bol
- Ophalen bij een bol afhaalpunt mogelijk
- 30 dagen bedenktijd en gratis retourneren
- Dag en nacht klantenservice
Rapporteer dit artikel
Je wilt melding doen van illegale inhoud over dit artikel:
- Ik wil melding doen als klant
- Ik wil melding doen als autoriteit of trusted flagger
- Ik wil melding doen als partner
- Ik wil melding doen als merkhouder
Geen klant, autoriteit, trusted flagger, merkhouder of partner? Gebruik dan onderstaande link om melding te doen.