mlpack_linear_regression(26 December 2016) | mlpack_linear_regression(26 December 2016) |
NAME¶
mlpack_linear_regression - simple linear regression and predictionSYNOPSIS¶
mlpack_linear_regression [-h] [-v]
DESCRIPTION¶
An implementation of simple linear regression and simple ridge regression using ordinary least squares. This solves the problemy = X * b + ewhere X (--input_file) and y (the last column of --input_file, or --input_responses) 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 (--lambda) greater than 0, which will regularize the covariance matrix to make it invertible. The calculated b is saved to disk (--output_file).
Optionally, the calculated value of b is used to predict the responses for another matrix X' (--test_file):
y' = X' * band these predicted responses, y', are saved to a file (--output_predictions). This type of regression is related to least-angle regression, which mlpack implements with the 'lars' executable.
OPTIONAL INPUT OPTIONS¶
- --help (-h)
- Default help info.
- --info [string]
- Get help on a specific module or option. Default value ''. --input_model_file (-m) [string] File containing existing model (parameters). 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]
- File containing X' (test regressors). Default value ''. --training_file (-t) [string] File containing training set X (regressors). Default value ''. --training_responses (-r) [string] Optional file containing y (responses). If not given, the responses are assumed to be the last row of the input file. Default value ''.
- --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.