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Springer Texts in Statistics Ser.: Introduction to Statistical Learning : With Applications in Python by Trevor Hastie, Gareth James, Robert Tibshirani, Jonathan Taylor and Daniela Witten (2023, Hardcover)
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- Buy it nowAn Introduction to Statistical Learning : With Applications in Py
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About this product
Product Identifiers
PublisherSpringer International Publishing A&G
ISBN-103031387465
ISBN-139783031387463
eBay Product ID (ePID)18061465611
Product Key Features
Number of PagesXv, 60 Pages
Publication NameIntroduction to Statistical Learning : with Applications in Python
LanguageEnglish
Publication Year2023
SubjectMathematical & Statistical Software, Probability & Statistics / General, General
TypeTextbook
AuthorTrevor Hastie, Gareth James, Robert Tibshirani, Jonathan Taylor, Daniela Witten
Subject AreaMathematics, Computers
SeriesSpringer Texts in Statistics Ser.
FormatHardcover
Dimensions
Item Weight52.8 Oz
Item Length10 in
Item Width7 in
Additional Product Features
Dewey Edition23
TitleLeadingAn
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal519.5
Table Of ContentIntroduction.- 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.
SynopsisAn 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.
LC Classification NumberQA276-280
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