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Top 10 Best Statistical Learning

of November 2024
1
Best ChoiceBest Choice
An Introduction to Statistical Learning: with Applications in R (Springer Texts
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
10
Exceptional
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2
Best ValueBest Value
The Elements of Statistical Learning: Data Mining, Inference, and Prediction,
Springer
Springer
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
9.9
Exceptional
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3
An Introduction to Statistical Learning: with Applications in R (Springer Texts
Springer
Springer
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
9.8
Exceptional
View on Amazon
4
Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on
Cambridge University Press
Cambridge University Press
Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics, Series Number 25)
9.7
Exceptional
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5
Statistical Learning with Sparsity (Chapman & Hall/CRC Monographs on Statistics
Chapman and Hall/CRC
Chapman and Hall/CRC
Statistical Learning with Sparsity (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
9.6
Exceptional
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6
The Elements of Statistical Learning: Data Mining, Inference, and Prediction,
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
9.5
Excellent
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7
A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in
Chapman and Hall/CRC
Chapman and Hall/CRC
A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)
9.4
Excellent
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8
Statistical Learning from a Regression Perspective (Springer Texts in
Springer
Springer
Statistical Learning from a Regression Perspective (Springer Texts in Statistics)
9.3
Excellent
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9
Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer
Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer Texts in Statistics)
9.2
Excellent
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10
Python for Probability, Statistics, and Machine Learning
Springer
Springer
Python for Probability, Statistics, and Machine Learning
9.1
Excellent
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About Statistical Learning

Click here to learn more about these products.

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Language Published English. Binding Hardcover. Comes in Good condition.

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

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.

Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics, Series Number 25)

Statistical Learning with Sparsity (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)

Statistical Learning from a Regression Perspective (Springer Texts in Statistics)

Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer Texts in Statistics)

Python for Probability, Statistics, and Machine Learning

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