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mlpack_lars - lars


mlpack_lars [-h] [-v] [-i string] [-m string] [-l double] [-L double] [-M string] [-o string] [-r string] [-t string] [-c] -V


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.


--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


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