mlpack_lars(1) - Linux man page

Name

lars - lars

Synopsis

 lars [-h] [-v] -i string -r string [-l double] [-L double] [-o string] -c

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).

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 lambda_1 > 0 and lambda_2 = 0, the problem is the LASSO. If lambda_1 > 0 and lambda_2 > 0, the problem is the Elastic Net. If lambda_1 = 0 and lambda_2 > 0, the problem is Ridge Regression. If lambda_1 = 0 and lambda_2 = 0, the problem is unregularized linear regression.

For efficiency reasons, it is not recommended to use this algorithm with lambda_1 = 0.

Required Options

--input_file (-i) [string]

File containing covariates (X).
--responses_file (-r) [string]: File containing y (responses/observations).

Options

--help (-h)

Default help info.
--info [string]: Get help on a specific module or option. 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_file (-o) [string]: File to save beta (linear estimator) to. Default value 'output.csv'.
--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.

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.