1mlpack_lars(1) General Commands Manual mlpack_lars(1)
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6 mlpack_lars - lars
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9 mlpack_lars [-h] [-v]
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12 An implementation of LARS: Least Angle Regression (Stagewise/laSso).
13 This is a stage-wise homotopy-based algorithm for L1-regularized linear
14 regression (LASSO) and L1+L2-regularized linear regression (Elastic
15 Net).
16 This program is able to train a LARS/LASSO/Elastic Net model or load a
18 model from file, output regression predictions for a test set, and save
19 the trained model to a file. The LARS algorithm is described in more
20 detail below:
21 Let X be a matrix where each row is a point and each column is a dimen‐
23 sion, and let y be a vector of targets.
24 The Elastic Net problem is to solve
26 min_beta 0.5 || X * beta - y ||_2^2 + lambda_1 ||beta||_1 +
28 0.5 lambda_2 ||beta||_2^2
29 If --lambda1 > 0 and --lambda2 = 0, the problem is the LASSO. If
31 --lambda1 > 0 and --lambda2 > 0, the problem is the Elastic Net. If
32 --lambda1 = 0 and --lambda2 > 0, the problem is ridge regression. If
33 --lambda1 = 0 and --lambda2 = 0, the problem is unregularized linear
34 regression.
35 For efficiency reasons, it is not recommended to use this algorithm
37 with --lambda_1 = 0. In that case, use the 'linear_regression' program,
38 which implements both unregularized linear regression and ridge regres‐
39 sion.
40 To train a LARS/LASSO/Elastic Net model, the --input_file and
42 --responses_file parameters must be given. The --lambda1 --lambda2, and
43 --use_cholesky arguments control the training parameters. A trained
44 model can be saved with the --output_model_file, or, if training is not
45 desired at all, a model can be loaded with --input_model_file. Any out‐
46 put predictions from a test file can be saved into the file specified
47 by the --output_predictions option.
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50 --help (-h)
51 Default help info.
52 --info [string]
54 Get help on a specific module or option. Default value ''.
55 --input_file (-i) [string]
57 File containing covariates (X). Default value ’'.
58 --input_model_file (-m) [string] File to load model from.
59 Default value ''.
60 --lambda1 (-l) [double]
62 Regularization parameter for l1-norm penalty. Default value 0.
63 --lambda2 (-L) [double]
65 Regularization parameter for l2-norm penalty. Default value 0.
66 --responses_file (-r) [string] File containing y
67 (responses/observations). Default value ''.
68 --test_file (-t) [string]
70 File containing points to regress on (test points). Default
71 value ''.
72 --use_cholesky (-c)
74 Use Cholesky decomposition during computation rather than
75 explicitly computing the full Gram matrix.
76 --verbose (-v)
78 Display informational messages and the full list of parameters
79 and timers at the end of execution.
80 --version (-V)
82 Display the version of mlpack.
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85 --output_model_file (-M) [string] File to save model to. Default value
86 ''. --output_predictions_file (-o) [string] If --test_file is speci‐
87 fied, this file is where the predicted responses will be saved. Default
88 value ''.
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92 For further information, including relevant papers, citations, and the‐
93 ory, For further information, including relevant papers, citations, and
94 theory, consult the documentation found at http://www.mlpack.org or
95 included with your consult the documentation found at
96 http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK.
97 DISTRIBUTION OF MLPACK.
98 mlpack_lars(1)