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368 – Methods and Applications in High-Dimensional Data, Part 1
Relaxnet and Widenet: Extending the glmnet R Package with Relaxation, Basis Expansions, and Aggressive Cross-Validation
Stephan Ritter
University of California at Berkeley
Alan Hubbard
University of California at Berkeley
Two new R packages for prediction will be described. relaxnet applies the idea of the Relaxed Lasso (Meinshausen, 2007) to glmnet models (as provided by the glmnet R package, Friedman et al, 2010), leading to increased prediction accuracy in certain cases, and greater sparsity of the selected model. widenet adds the capability of applying polynomial basis expansion to the input data and then selecting a subset of the basis functions using relaxnet. The intent with both of these packages is for the user to make aggressive use of cross-validation to select tuning parameters, and this is encouraged by providing options to easily parallelize the execution over different parameter values and over cross-validation folds.