Provided by: mlpack-bin_3.0.4-1_amd64

**NAME**

mlpack_lars- lars

**SYNOPSIS**

mlpack_lars[-istring] [-munknown] [-ldouble] [-Ldouble] [-rstring] [-tstring] [-cbool] [-Vbool] [-Munknown] [-ostring] [-h-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 ’--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': $ lars--input_filedata.csv--responses_fileresponses.csv--lambda10.4--lambda20--output_model_filelasso_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': $ lars--input_model_filelasso_model.bin--test_filetest.csv--output_predictions_filetest_predictions.csv

**OPTIONAL** **INPUT** **OPTIONS**

--help(-h)[bool]Default help info.--info[string]Get help on a specific module or option. Default value ''.--input_file(-i)[string]Matrix of covariates (X). Default value ''.--input_model_file(-m)[unknown]Trained LARS model to use. 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.--responses_file(-r)[string]Matrix of responses/observations (y). Default value ''.--test_file(-t)[string]Matrix containing points to regress on (test points). Default value ''.--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.

**OPTIONAL** **OUTPUT** **OPTIONS**

--output_model_file(-M)[unknown]Output LARS model. Default value ''.--output_predictions_file(-o)[string]If--test_fileis specified, this file is where the predicted responses will be saved. Default value ''.

**ADDITIONAL** **INFORMATION**

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.