mlpack_linear_regression

NAME

mlpack_linear_regression - simple linear regression and prediction

SYNOPSIS

mlpack_linear_regression [-h] [-v]

DESCRIPTION

An implementation of simple linear regression and simple ridge regression using ordinary least squares. This solves the problem

y = X * b + e

where X (specified by ’--training_file (-t)’) and y (specified either as the last column of the input matrix ’--training_file (-t)’ or via the ’--training_responses_file (-r)’ parameter) are known and b is the desired variable. If the covariance matrix (X’X) is not invertible, or if the solution is overdetermined, then specify a Tikhonov regularization constant (with ’--lambda (-l)’) greater than 0, which will regularize the covariance matrix to make it invertible. The calculated b may be saved with the ’--output_predictions_file (-o)’ output parameter.

Optionally, the calculated value of b is used to predict the responses for another matrix X’ (specified by the ’--test_file (-T)’ parameter):

y’ = X’ * b

and the predicted responses y’ may be saved with the ’--output_predictions_file (-o)’ output parameter. This type of regression is related to least-angle regression, which mlpack implements as the ’lars’ program.

For example, to run a linear regression on the dataset ’X.csv’ with responses ’y.csv’, saving the trained model to ’lr_model.bin’, the following command could be used:

$ linear_regression --training_file X.csv --training_responses_file y.csv --output_model_file lr_model.bin

Then, to use ’lr_model.bin’ to predict responses for a test set ’X_test.csv’, saving the predictions to ’X_test_responses.csv’, the following command could be used:

$ linear_regression --input_model_file lr_model.bin --test_file X_test.csv --output_predictions_file X_test_responses.csv

OPTIONAL INPUT OPTIONS

--help (-h) [bool]

Default help info.

--info [string]

Get help on a specific module or option. Default value ’’. --input_model_file (-m) [string] Existing LinearRegression model to use. Default value ’’.

--lambda (-l) [double]

Tikhonov regularization for ridge regression. If 0, the method reduces to linear regression. Default value 0.

--test_file (-T) [string]

Matrix containing X’ (test regressors). Default value ’’. --training_file (-t) [string] Matrix containing training set X (regressors). Default value ’’. --training_responses_file (-r) [string] Optional vector containing y (responses). If not given, the responses are assumed to be the last row of the input file. Default value ’’.

--verbose (-v) [bool]

Display informational messages and the full list of parameters and timers at the end of execution.

--version (-V) [bool]

Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

--output_model_file (-M) [string] Output LinearRegression model. Default value ’’. --output_predictions_file (-o) [string] If --test_file is specified, this matrix is where the predicted responses will be saved. Default value ’’.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

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.