mlpack_softmax_regression - softmax regression

       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_softmax_regression(1)

       This program performs softmax regression, a generalization of logistic regression to the multiclass case,
       and  has support for L2 regularization. The program is able to train a model, load an existing model, and
       give predictions (and optionally their accuracy) for test data.

       Training a softmax regression model is done by giving a file of training points with the '--training_file
       (-t)' parameter and their corresponding labels with the '--labels_file (-l)'  parameter.  The  number  of
       classes  can  be manually specified with the '--number_of_classes (-c)' parameter, and the maximum number
       of iterations of the L-BFGS optimizer can be specified with the ’--max_iterations (-n)' parameter. The L2
       regularization constant can be specified with the '--lambda (-r)' parameter and if an intercept  term  is
       not desired in the model, the '--no_intercept (-N)' parameter can be specified.

       The  trained  model can be saved with the '--output_model_file (-M)' output parameter. If training is not
       desired, but only testing is, a model can be loaded with the '--input_model_file (-m)' parameter. At  the
       current  time, a loaded model cannot be trained further, so specifying both '--input_model_file (-m)' and
       '--training_file (-t)' is not allowed.

       The program is also able to evaluate a model on test data. A test  dataset  can  be  specified  with  the
       '--test_file  (-T)'  parameter.  Class predictions can be saved with the '--predictions_file (-p)' output
       parameter. If labels are specified for the test data with the '--test_labels_file (-L)'  parameter,  then
       the  program  will  print  the  accuracy  of  the predictions on the given test set and its corresponding
       labels.

       For example, to train a softmax regression model on the data 'dataset.csv' with labels 'labels.csv'  with
       a  maximum  of  1000  iterations  for training, saving the trained model to 'sr_model.bin', the following
       command can be used:

              $    mlpack_softmax_regression--training_file     dataset.csv     --labels_file     labels.csv
              --output_model_file sr_model.bin

              Then,  to  use  'sr_model.bin' to classify the test points in 'test_points.csv', saving the output
              predictions to 'predictions.csv', the following command can be used:

              $   mlpack_softmax_regression--input_model_file   sr_model.bin   --test_file    test_points.csv
              --predictions_file predictions.csv

mlpack_softmax_regression - softmax regression

--help(-h)[bool]
              Default help info.

       --info[string]
              Print help on a specific option. Default value ''.

       --input_model_file(-m)[unknown]
              File  containing  existing  model  (parameters).  --labels_file (-l) [unknown] A matrix containing
              labels (0 or 1) for the points in the training set (y). The labels must order as a row.

       --lambda(-r)[double]
              L2-regularization constant Default value 0.0001.

       --max_iterations(-n)[int]
              Maximum number of iterations before termination. Default value 400.

       --no_intercept(-N)[bool]
              Do not add the intercept term to the model.

       --number_of_classes(-c)[int]
              Number of classes for classification; if unspecified (or 0), the number of classes  found  in  the
              labels will be used. Default value 0.

       --test_file(-T)[unknown]
              Matrix containing test dataset.

       --test_labels_file(-L)[unknown]
              Matrix containing test labels.

       --training_file(-t)[unknown]
              A matrix containing the training set (the matrix of predictors, X).

       --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]
              File to save trained softmax regression model to.

       --predictions_file(-p)[unknown]
              Matrix to save predictions for test dataset into.

       --probabilities_file(-P)[unknown]
              Matrix to save class probabilities for test dataset into.

mlpack_softmax_regression [-munknown] [-lunknown] [-rdouble] [-nint] [-Nbool] [-cint] [-Tunknown] [-Lunknown] [-tunknown] [-Vbool] [-Munknown] [-punknown] [-Punknown] [-h-v]