This tutorial with real R code demonstrates how to create a predictive model using cforest (Breiman’s random forests) from the package party, evaluate the predictive model on a separate set of data, and then plot the performance using ROC curves ...

A Beautiful WWW put together a great set of resources for getting started with machine learning in R. First, they recommend the previously mentioned free book, The Elements of Statistical Learning. Then there's a link to a list of dozens of machine learning and statistical learning packages for R. Next, you'll need data. Hundreds of free real datasets are...

This post will eventually grow to hold a wide list of books on statistics (e-books, pdf books and so on) that are available for free download. But for now we’ll start off with just one several books: The Elements of Statistical Learning written by Trevor Hastie, Robert Tibshirani and Jerome Friedman. you can legally download

Revolutions blog recently posted a link to R code by Joshua Reich with self-contained examples of using machine learning techniques in R, including various clustering methods (k-means, nearest neighbor, and kernel), recursive partitioning (CART), principle components analysis, linear discriminant analysis, and support vector machines. This post also links to some slides that go over the basics

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