NAME

       mlpack_lars - lars

SYNOPSIS

        mlpack_lars [-h] [-v] [-i string] [-m string] [-l double] [-L double] [-M string] [-o string] [-r string] [-t string] [-c] -V

DESCRIPTION

       An  implementation of LARS: Least Angle Regression (Stagewise/laSso). This is a stage-wise
       homotopy-based   algorithm   for   L1-regularized   linear    regression    (LASSO)    and
       L1+L2-regularized linear regression (Elastic Net).

       This  program  is  able to train a LARS/LASSO/Elastic Net model or load a model from file,
       output regression predictions for a test set, and save the trained model to  a  file.  The
       LARS algorithm is described in more detail below:

       Let X be a matrix where each row is a point and each column is a dimension, and let y be a
       vector of targets.

       The Elastic Net problem is to solve

         min_beta 0.5 || X * beta - y ||_2^2 + lambda_1 ||beta||_1 +
           0.5 lambda_2 ||beta||_2^2

       If --lambda1 > 0 and --lambda2 = 0, the problem is  the  LASSO.   If  --lambda1  >  0  and
       --lambda2  >  0,  the problem is the Elastic Net.  If --lambda1 = 0 and --lambda2 > 0, the
       problem is ridge regression.  If  --lambda1  =  0  and  --lambda2  =  0,  the  problem  is
       unregularized linear regression.

       For  efficiency  reasons, it is not recommended to use this algorithm with --lambda_1 = 0.
       In that case, use the 'linear_regression' program,  which  implements  both  unregularized
       linear regression and ridge regression.

       To  train a LARS/LASSO/Elastic Net model, the --input_file and --responses_file parameters
       must be given. The --lambda1 --lambda2, and --use_cholesky arguments control the  training
       parameters.  A trained model can be saved with the --output_model_file, or, if training is
       not desired at all, a model can be loaded with --input_model_file. Any output  predictions
       from a test file can be saved into the file specified by the --output_predictions option.

OPTIONS

       --help (-h)
              Default help info.

       --info [string]
              Get help on a specific module or option.  Default value ''.

       --input_file (-i) [string]
              File containing covariates (X). Default value ’'.  --input_model_file (-m) [string]
              File to load model from. Default value ''.

       --lambda1 (-l) [double]
              Regularization parameter for l1-norm penalty.  Default value 0.

       --lambda2 (-L) [double]
              Regularization   parameter    for    l2-norm    penalty.     Default    value    0.
              --output_model_file  (-M)  [string]  File  to  save  model  to.  Default  value ''.
              --output_predictions (-o) [string] If --test_file is specified, this file is  where
              the   predicted   responses   will   be  saved.  Default  value  'predictions.csv'.
              --responses_file (-r) [string] File containing y (responses/observations).  Default
              value ''.

       --test_file (-t) [string]
              File containing points to regress on (test points). Default value ''.

       --use_cholesky (-c)
              Use  Cholesky decomposition during computation rather than explicitly computing the
              full Gram matrix.

       --verbose (-v)
              Display informational messages and the full list of parameters and  timers  at  the
              end of execution.

       --version (-V)
              Display the version of mlpack.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

       For  further  information,  including  relevant papers, citations, and theory, For further
       information, including relevant papers, citations, and theory, consult  the  documentation
       found  at  http://www.mlpack.org  or included with your consult the documentation found at
       http://www.mlpack.org or included with  your  DISTRIBUTION  OF  MLPACK.   DISTRIBUTION  OF
       MLPACK.

                                                                                   mlpack_lars(1)