An Introduction to Statistical Learning: With Applications in R (Hardcover)

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

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商品描述

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, 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.

商品描述(中文翻譯)

《統計學習導論》提供了對統計學習領域的易於理解的概述,這是一個在過去二十年中在生物學、金融學、市場營銷學和天體物理學等領域中出現的龐大而複雜的數據集的必備工具集。本書介紹了一些最重要的建模和預測技術,以及相關應用。主題包括線性回歸、分類、重抽樣方法、收縮方法、基於樹的方法、支持向量機、聚類等。使用彩色圖形和實際示例來說明所介紹的方法。由於本教科書的目標是幫助科學、工業和其他領域的從業人員使用這些統計學習技術,因此每章都包含了在R中實施分析和方法的教程,R是一個非常流行的開源統計軟件平台。

本書的兩位作者之一與Hastie、Tibshirani和Friedman合著了《統計學習的要素》(第二版,2009年),這是一本統計學和機器學習研究人員廣泛參考的書籍。《統計學習導論》涵蓋了許多相同的主題,但面向的受眾更廣泛。本書針對統計學家和非統計學家,他們希望使用尖端的統計學習技術來分析他們的數據。本書假設只有一門線性回歸的先修課程,並且不需要矩陣代數的知識。