Introduction to Statistical Learning - GitHub Pages
As a textbook for an introduction to data science through machine learning, there is much to like about ISLR. It’s thorough, lively, written at level appropriate for undergraduates and usable by nonexperts.
Introduction to Statistical Learning - GitHub Pages
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 to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, …
Introduction to Statistical Learning - GitHub Pages
Introduction to Statistical LearningGareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Introduction to Statistical Learning - GitHub Pages
An Introduction to Statistical Learning has now been published by Springer. The book can be purchased at Amazon or directly from Springer.
Introduction to Statistical Learning - GitHub Pages
If you use any of these figures in a presentation or lecture, somewhere in your set of slides please add the paragraph: "Some of the figures in this presentation are taken from "An Introduction to Statistical Learning, with applications in R" (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani " If you wish to use any of these figures in a ...
Introduction to Statistical Learning - GitHub Pages
Gareth James is a professor of data sciences and operations at University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area. Daniela Witten is an assistant …
Introduction to Statistical Learning - GitHub Pages
Errata for the 1st Edition, since the 7th printing (June 2017) and not reflected in online version Page 254. The glmnet package has been updated so two lines of code need to change. The line ridge.pred=predict (ridge.mod,s=0,newx=x [test,],exact=T) should be changed to ridge.pred=predict (ridge.mod,s=0,newx=x [test,],exact=T,x=x [train,],y=y [train]) In addition the …
Statistical Learning with Sparsity: the Lasso and Generalizations
A comprehensive guide to statistical learning with a focus on Lasso and its generalizations.
Statistical Learning with Sparsity: the Lasso and Generalizations
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set.seed(17) cv.error.10=rep(0,10) for (i in 1:10){ glm.fit=glm(mpg~poly(horsepower,i),data=Auto) cv.error.10[i]=cv.glm(Auto,glm.fit,K=10)$delta[1] } cv.error.10