best_model.sgl

object

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

Fast generic solver for sparse group lasso optimization
problems. The loss (objective) function must be defined in a
C++ module. The optimization problem is solved using a
coordinate gradient descent algorithm. Convergence of the
algorithm is established (see reference) and the algorithm is
applicable to a broad class of loss functions. 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

sglOptim

best_model.sgl: Index of best model

Description

Usage

Arguments

Value