An Introduction to Statistical Learning : with Applications in Python / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor.

Format:

Book

Author/Creator:

James, Gareth, 1936- author.

Series:

Springer Texts in Statistics, 2197-4136

Language:

English

Subjects (All):

Statistics.

Mathematical statistics--Data processing.

Mathematical statistics.

Statistical Theory and Methods.

Statistics and Computing.

Applied Statistics.

Local Subjects:

Statistical Theory and Methods.

Statistics and Computing.

Applied Statistics.

Physical Description:

1 online resource (617 pages)

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

Summary:

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.

Contents:

Introduction

Statistical Learning

Linear Regression

Classification

Resampling Methods

Linear Model Selection and Regularization

Moving Beyond Linearity

Tree-Based Methods

Support Vector Machines

Deep Learning

Survival Analysis and Censored data

Unsupervised Learning

Multiple Testing

Index.

Notes:

Includes index.

Other Format:

Print version: James, Gareth An Introduction to Statistical Learning

ISBN:

9783031387470

3031387473