Description: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 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. Shipping We offer FREE shipping on specialized orders! We ship within Three business days of payment, usually sooner. We use a selection of shipping services such as UPS, FedEx, USPS etc. We only ship to the lower 48 states, no APO/FPO addresses or PO Boxes allowed. Local pickups and combined shipping options are not provided at this time. Return You can return a product for up to 30 days from the date you purchased it. Any product you return must be in the same condition you received it and in the original packaging. Please keep the receipt. Payment We accept payment by any of the following methods:PayPalPlease pay as soon as possible after winning an auction, as that will allow us to post your item to you sooner!Credit/Debit CardPlease pay within 2 days of buying now, as it makes it easier to ship as fast as possible to you! Feedback Customer satisfaction is very important to us. If you have any problem with your order, please contact us and we will do our best to make you satisfied. Contact Us If you have any queries, please contact us via ebay. We usually respond within 24 hours on weekdays. Please visit our eBay store to check out other items for sale! Thank you for shopping at our store.
Price: 75.37 USD
Location: San Gabriel, California
End Time: 2024-11-03T00:38:09.000Z
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EAN: 9781071614204
ISBN: 9781071614204
Package Dimensions LxWxH: 9.29x6.06x1.54 Inches
Weight: 2.07 Pounds
MPN: Does not apply
Model: Does not apply
Brand: None
Item Length: 9.3in
Item Width: 6.1in
Author: Trevor Hastie, Gareth James, Robert Tibshirani, Daniela Witten
Publication Name: Introduction to Statistical Learning : with Applications in R
Format: Trade Paperback
Language: English
Publisher: Springer
Publication Year: 2022
Series: Springer Texts in Statistics Ser.
Type: Textbook
Item Weight: 33.3 Oz
Number of Pages: Xv, 607 Pages