Provided by: mlpack-bin_3.0.4-1_amd64 bug


       mlpack_linear_regression - simple linear regression and prediction


        mlpack_linear_regression [-m unknown] [-l double] [-T string] [-t string] [-r string] [-V bool] [-M unknown] [-o string] [-h -v]


       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


       --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.


       --output_model_file (-M) [unknown]
              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 ''.


       For further information, including relevant papers, citations,  and  theory,  consult  the
       documentation found at or included with your distribution of mlpack.