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An Introduction To Statistical Learning With Applications In R

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Dernière mise à jour : 11 septembre 2025
Springer
Springer
An Introduction to Statistical Learning: With Applications in R
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Springer
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An Introduction to Statistical Learning: with Applications in R
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  • This book presents some of the most important modeling and prediction techniques, along with relevant applications
  • Topics include linear regression, classification, re-sampling 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
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Springer International Publishing AG
Springer International Publishing AG
An Introduction to Statistical Learning: With Applications in Python
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An Introduction to Statistical Learning. With Applications in R
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A Solution Manual and Notes for: An Introduction to Statistical Learning: with Applications in R: Machine Learning (English Edition)
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Springer
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Introduction to Image Processing Using R: Learning by Examples (SpringerBriefs in Computer Science) (English Edition)
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an introduction to statistical learning with applications in python