mlpack_lars man page

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

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.

Info