mlpack_linear_regression - simple linear regression and prediction

       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.

mlpack-4.5.1                                     29 January 2025                     mlpack_linear_regression(1)

       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

mlpack_linear_regression - simple linear regression and prediction

--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.

--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.

mlpack_linear_regression [-munknown] [-ldouble] [-Tunknown] [-tunknown] [-runknown] [-Vbool] [-Munknown] [-ounknown] [-h-v]