An Introduction to Statistical Learning: With Applications in Python
James, Gareth, Witten, Daniela, Hastie, Trevor
相關主題
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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.
Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
商品描述(中文翻譯)
《統計學習導論》提供了統計學習領域的易懂概述，這是一套對於理解過去二十年在生物學、金融、行銷和天體物理等領域出現的龐大且複雜數據集至關重要的工具。本書介紹了一些最重要的建模和預測技術，以及相關的應用。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、深度學習、生存分析、多重測試等。書中使用彩色圖形和真實世界的例子來說明所介紹的方法。本書的目標讀者是統計學家和非統計學家，旨在使用尖端的統計學習技術來分析他們的數據。
四位作者共同撰寫的《統計學習導論：R應用》（ISLR）已成為全球本科和研究生課堂的主流教材，也是數據科學家重要的參考書籍。其成功的關鍵之一在於每一章都包含了在R科學計算環境中實施所介紹的分析和方法的教程。然而，近年來Python已成為數據科學的熱門語言，對於基於Python的ISLR替代品的需求日益增加。因此，本書（ISLP）涵蓋了與ISLR相同的內容，但實驗室部分則使用Python實現。這些實驗室對於Python新手和經驗豐富的用戶都將非常有用。
作者簡介
Gareth James is the John H. Harland Dean of Goizueta Business School at Emory University. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book with that title. Hastie co-developed much of the statistical modeling software and environment in R, and invented principal curves and surfaces. Tibshirani invented the lasso and is co-author of the very successful book, An Introduction to the Bootstrap. They are both elected members of the US National Academy of Sciences.
Jonathan Taylor is a professor of statistics at Stanford University. His research focuses on selective inference and signal detection in structured noise.
作者簡介(中文翻譯)
Gareth James 是艾默里大學 Goizueta 商學院的 John H. Harland 院長。他在統計學習領域發表了大量的方法論研究，特別強調高維度和函數數據。本書的概念框架源自他在該領域的 MBA 選修課程。
Daniela Witten 是華盛頓大學的統計學和生物統計學教授，以及 Dorothy Gilford 受贈講座教授。她的研究主要集中在統計機器學習技術，用於分析複雜、混亂和大規模數據，特別強調無監督學習。
Trevor Hastie 和 Robert Tibshirani 是斯坦福大學的統計學教授，也是成功教科書《統計學習要素》的共同作者。Hastie 和 Tibshirani 開發了廣義加法模型，並撰寫了一本以此為題的熱門書籍。Hastie 共同開發了 R 語言中的許多統計建模軟體和環境，並發明了主成分曲線和曲面。Tibshirani 創造了套索（lasso），並是非常成功的書籍《自助法導論》的共同作者。他們都是美國國家科學院的當選成員。
Jonathan Taylor 是斯坦福大學的統計學教授。他的研究專注於結構噪聲中的選擇性推斷和信號檢測。
