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πŸ˜‡, πŸ˜‡An Introduction to Statistical Learning: With Applications, HARDCOVER

Description: An Introduction to Statistical Learning: With Applications in R, HARDCOVER... 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 naΓ―ve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility. NOTES Import Duties or Taxes : Please Note Import Duties taxes, Vat, quarantine fees, address change fees, or any other taxes are not included in the item price or in shipping charges. These charges are the buyer's responsibility. You are requested to please check with your country's custom office to determine what these additional cost or taxes etc before bidding / buying this item(s) We do not have any control on customs charges or custom clearance time or on any other charges; hence, the delivery time is for reference only. Sellers are not responsible for shipping service transit times. Transit times may vary particularly during peak periods. Customs duty fees are normally charged by the shipping company or collected when they deliver the parcels. Feedback : If you feel any problem in product please immediately contact us as we ensure fast and best solution for any problem got in our product Thanks for Visiting our Store.

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End Time: 2024-09-30T09:38:21.000Z

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πŸ˜‡, πŸ˜‡An Introduction to Statistical Learning: With Applications, HARDCOVERπŸ˜‡, πŸ˜‡An Introduction to Statistical Learning: With Applications, HARDCOVERπŸ˜‡, πŸ˜‡An Introduction to Statistical Learning: With Applications, HARDCOVER

Item Specifics

Return shipping will be paid by: Buyer

All returns accepted: Returns Accepted

Item must be returned within: 14 Days

Refund will be given as: Money Back

Series: Springer Texts in Statistics Ser.

Publication Year: 2021

Type: Textbook

Format: Hardcover

Language: English

Publication Name: Introduction to Statistical Learning : with Applications in R

Author: Trevor Hastie, Gareth James, Robert Tibshirani, Daniela Witten

Item Length: 9.3in

Subject: Statistics

Item Width: 6.1in

Item Weight: 42 Oz

Number of Pages: Xv, 607 Pages

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