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Statistical Learning from a Regression Perspective (Springer Texts in Statistics) by Richard A. Berk (auth.) is a nonfiction available to read on EtoBox.

What is Statistical Learning from a Regression Perspective (Springer Texts in Statistics) about?

This Textbook Considers Statistical Learning Applications When Interest Centers On The Conditional Distribution Of The Response Variable, Given A Set Of Predictors, And When It Is Important To Characterize How The Predictors Are Related To The Response. As A First Approximation, This Can Be Seen As An Extension Of Nonparametric Regression. This Fully Revised New Edition Includes Important Developments Over The Past 8 Years. Consistent With Modern Data Analytics, It Emphasizes That A Proper Statistical Learning Data Analysis Derives From Sound Data Collection, Intelligent Data Management, Appropriate Statistical Procedures, And An Accessible Interpretation Of Results. A Continued Emphasis On The Implications For Practice Runs Through The Text. Among The Statistical Learning Procedures Examined Are Bagging, Random Forests, Boosting, Support Vector Machines And Neural Networks. Response Variables May Be Quantitative Or Categorical.^ As In The First Edition, A Unifying Theme Is Supervised Learning That Can Be Treated As A Form Of Regression Analysis. Key Concepts And Procedures Are Illustrated With Real Applications, Especially Those With Practical Implications. A Principal Instance Is

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

Author

Richard A. Berk (auth.)

Publisher

Springer International Publishing : Imprint : Springer

Published

2016

Language

EN

ISBN

9783319440484

Category

nonfiction

Subjects

Psychology, Education, Mathematics