Description: "As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis. "The first edition of this book, with applications in R (ISLR), was released in 2013. A 2nd Edition of ISLR was published in 2021. It has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian, and Vietnamese. The Python edition (ISLP) was published in 2023."Each edition contains a lab at the end of each chapter, which demonstrates the chapter’s concepts in either R or Python. "The chapters cover the following topics:What is statistical learning?RegressionClassificationResampling methodsLinear model selection and regularizationMoving beyond linearity Tree-based methodsSupport vector machinesDeep learningSurvival analysisUnsupervised learningMultiple testing"(From the publisher)
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Item Specifics
All returns accepted: ReturnsNotAccepted
Number of Pages: Xiv, 426 Pages
Language: English
Publication Name: Introduction to Statistical Learning : with Applications in R
Publisher: Springer New York
Subject: Mathematical & Statistical Software, Intelligence (Ai) & Semantics, Probability & Statistics / General
Item Height: 0.9 in
Publication Year: 2017
Type: Textbook
Item Weight: 35.8 Oz
Author: Trevor Hastie, Gareth James, Robert Tibshirani, Daniela Witten
Item Length: 9.5 in
Subject Area: Mathematics, Computers
Item Width: 6.4 in
Series: Springer Texts in Statistics Ser.
Format: Hardcover
