mlpack_lars - lars

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

mlpack-4.5.1                                     29 January 2025                                  mlpack_lars(1)

       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 ’--lambda1 (-l)' = 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 (-i)' and ’--responses_file (-r)' parameters
       must be given. The '--lambda1 (-l)', ’--lambda2 (-L)', and '--use_cholesky (-c)' parameters  control  the
       training  options.  A  trained model can be saved with the '--output_model_file (-M)'.  If no training is
       desired at all, a model can be passed via the ’--input_model_file (-m)' parameter.

       The program can also provide predictions for test data using either the trained model or the given  input
       model.  Test  points  can  be specified with the ’--test_file (-t)' parameter. Predicted responses to the
       test points can be saved with the '--output_predictions_file (-o)' output parameter.

       For example, the following command trains a model on the data 'data.csv'  and  responses  'responses.csv'
       with  lambda1 set to 0.4 and lambda2 set to 0 (so, LASSO is being solved), and then the model is saved to
       'lasso_model.bin':

       $  mlpack_lars--input_file  data.csv  --responses_file  responses.csv   --lambda1   0.4   --lambda2   0
       --output_model_file lasso_model.bin

       The  following  command uses the 'lasso_model.bin' to provide predicted responses for the data 'test.csv'
       and save those responses to ’test_predictions.csv':

       $  mlpack_lars--input_model_file   lasso_model.bin   --test_file   test.csv   --output_predictions_file
       test_predictions.csv

mlpack_lars - lars

--help(-h)[bool]
              Default help info.

       --info[string]
              Print help on a specific option. Default value ''.

       --input_file(-i)[unknown]
              Matrix of covariates (X).

       --input_model_file(-m)[unknown]
              Trained LARS model to use.

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

       --lambda2(-L)[double]
              Regularization parameter for l2-norm penalty.  Default value 0.

       --no_intercept(-n)[bool]
              Do not fit an intercept in the model.

       --no_normalize(-N)[bool]
              Do not normalize data to unit variance before modeling.

       --responses_file(-r)[unknown]
              Matrix of responses/observations (y).

       --test_file(-t)[unknown]
              Matrix containing points to regress on (test points).

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

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

       --version(-V)[bool]
              Display the version of mlpack.

--output_model_file(-M)[unknown]
              Output LARS model.

       --output_predictions_file(-o)[unknown]
              If --test_file is specified, this file is where the predicted responses will be saved.

mlpack_lars [-iunknown] [-munknown] [-ldouble] [-Ldouble] [-nbool] [-Nbool] [-runknown] [-tunknown] [-cbool] [-Vbool] [-Munknown] [-ounknown] [-h-v]