mlpack_linear_regression(1)

NAME
mlpack_linear_regression - simple linear regression and prediction

SYNOPSIS
 mlpack_linear_regression [-m unknown] [-l double] [-T unknown] [-t unknown] [-r unknown] [-V bool] [-M unknown] [-o unknown] [-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:

$ mlpack_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:

$ mlpack_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]
    Print help on a specific option. Default value ''.

--input_model_file (-m) [unknown]
    Existing LinearRegression model to use.

--lambda (-l) [double]
    Tikhonov regularization for ridge regression. If 0, the method reduces to linear regression. Default value 0.

--test_file (-T) [unknown]
    Matrix containing X' (test regressors).

--training_file (-t) [unknown]
    Matrix containing training set X (regressors).

--training_responses_file (-r) [unknown]
    Optional vector containing y (responses). If not given, the responses are assumed to be the last row of the input file.

--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) [unknown]
    Output LinearRegression model.

--output_predictions_file (-o) [unknown]
    If --test_file is specified, this matrix is where the predicted responses will be saved.nnADDITIONAL INFORMATIONNN 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.