predict.msgl

an object of class msgl, produced with
  <code>msgl</code>.

object

a data matrix of size $N_\textrm{new} \times
  p$.

if TRUE <code>x</code> will be treated as
  sparse, if <code>x</code> is a sparse matrix it will be treated
  as sparse by default.

sparse.data

Computes the linear predictors, the estimated
  probabilities and the estimated classes for a new data
  set.

Sparse group lasso multiclass classification, suitable for
        high dimensional problems with many classes. Fast algorithm for
        solving the multinomial sparse group lasso convex optimization
        problem. This package apply template metaprogramming
        techniques, therefore -- when compiling the package from source
        -- a high level of optimization is needed to gain full speed
        (e.g. for the GCC compiler use -O3). Use of multiple processors
        for cross validation and subsampling is supported through
        OpenMP. The Armadillo C++ library is used as the primary linear
        algebra engine. Armadillo is licensed under the MPL 2.0. The
        Armadillo C++ library is primarily developed at NICTA
        (Australia) by Conrad Sanderson, with contributions from around
        the world. Furthermore the package utilize various Boost
        libraries, in particular the Tuple library by Jaakko Jarvi and
        the Random library by Jens Maurer. The Boost libraries are
        licensed under the Boost Software License.

predict.msgl: Predict

Description

Usage

Arguments

Value