Provided by: mlpack-bin_3.0.4-1_amd64

**NAME**

mlpack_linear_regression- simple linear regression and prediction

**SYNOPSIS**

mlpack_linear_regression[-munknown] [-ldouble] [-Tstring] [-tstring] [-rstring] [-Vbool] [-Munknown] [-ostring] [-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_fileX.csv--training_responses_filey.csv--output_model_filelr_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_filelr_model.bin--test_fileX_test.csv--output_predictions_fileX_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)[unknown]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)[unknown]Output LinearRegression model. Default value ''.--output_predictions_file(-o)[string]If--test_fileisspecified,thismatrixiswherethepredictedresponseswillbesaved.Default value ''.

**ADDITIONAL** **INFORMATION**

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.