best_model.msgl

object

<p>Returns the index of the best model, in terms of lowest error rate</p>

Multinomial logistic regression with sparse group lasso
penalty. Simultaneous feature selection and parameter
estimation for classification. Suitable for high dimensional
multiclass classification with many classes. The algorithm
computes the sparse group lasso penalized maximum likelihood
estimate. Use of parallel computing for cross validation and
subsampling is supported through the 'foreach' and 'doParallel'
packages. Development version is on GitHub, please report
package issues on GitHub.

Niels Richard Hansen

msgl

best_model.msgl: Index of best model

Description

Usage

Arguments

Value