# mlpack_lars - Online in the Cloud

This is the command mlpack_lars that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator

### PROGRAM:

#### 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.

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)

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