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Название: An Introduction to Statistical Learning: with Applications in PythonАвтор: Gareth James, Daniela Witten, Trevor Hastie
Издательство: Springer
Год: 2023
Страниц: 617
Язык: английский
Формат: pdf (true)
Размер: 12.6 MB
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. However, in recent years Python has become an increasingly popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book, An Introduction to Statistical Learning, With Applications in Python (ISLP), covers the same materials as ISLR but with labs implemented in Python — a feat accomplished by the addition of a new co-author, Jonathan Taylor.
