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)