lars(1) lars


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

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. In that case, use the 'linear_regression' program, which implements both unregularized linear regression and ridge regression.


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


--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.
--version (-V)
Display the version of mlpack.


For further information, including relevant papers, citations, and theory, consult the documentation found at or included with your distribution of MLPACK.