NAME

       mlpack_nbc - parametric naive bayes classifier

SYNOPSIS

        mlpack_nbc [-I bool] [-m unknown] [-l unknown] [-T unknown] [-t unknown] [-V bool] [-o unknown] [-M unknown] [--output_probs_file unknown] [-a unknown] [-p unknown] [-h -v]

DESCRIPTION

       This  program  trains the Naive Bayes classifier on the given labeled training set, or loads a model from
       the given model file, and then may use that trained model to classify the points in a given test set.

       The training set is specified with the '--training_file (-t)' parameter.  Labels may be either  the  last
       row  of  the  training  set, or alternately the ’--labels_file (-l)' parameter may be specified to pass a
       separate matrix of labels.

       If training is not desired, a pre-existing  model  may  be  loaded  with  the  ’--input_model_file  (-m)'
       parameter.

              The  '--incremental_variance  (-I)'  parameter  can  be  used  to  force  the  training  to use an
              incremental algorithm for calculating variance. This  is  slower,  but  can  help  avoid  loss  of
              precision in some cases.

              If  classifying  a  test set is desired, the test set may be specified with the ’--test_file (-T)'
              parameter, and the classifications may be  saved  with  the  ’--predictions_file  (-a)'predictions
              parameter.  If saving the trained model is desired, this may be done with the '--output_model_file
              (-M)' output parameter.

              Note: the '--output_file (-o)' and '--output_probs_file' parameters are  deprecated  and  will  be
              removed in mlpack 4.0.0. Use '--predictions_file (-a)' and '--probabilities_file (-p)' instead.

              For  example, to train a Naive Bayes classifier on the dataset 'data.csv' with labels 'labels.csv'
              and save the model to 'nbc_model.bin', the following command may be used:

              $ mlpack_nbc --training_file data.csv --labels_file labels.csv --output_model_file nbc_model.bin

              Then, to use 'nbc_model.bin' to predict the classes of the dataset  ’test_set.csv'  and  save  the
              predicted classes to 'predictions.csv', the following command may be used:

              $    mlpack_nbc    --input_model_file   nbc_model.bin   --test_file   test_set.csv   --output_file
              predictions.csv

OPTIONAL INPUT OPTIONS

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

       --incremental_variance (-I) [bool]
              The variance of each class will be calculated incrementally.

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

       --input_model_file (-m) [unknown]
              Input Naive Bayes model.  --labels_file (-l) [unknown] A file containing labels for  the  training
              set.

       --test_file (-T) [unknown]
              A matrix containing the test set.

       --training_file (-t) [unknown]
              A matrix containing the training set.

       --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_file  (-o)  [unknown]  The matrix in which the predicted labels for the test set will be written
       (deprecated).

       --output_model_file (-M) [unknown]
              File to save trained Naive Bayes model to.

       --output_probs_file [unknown]
              The matrix in which the predicted  probability  of  labels  for  the  test  set  will  be  written
              (deprecated).

       --predictions_file (-a) [unknown]
              The matrix in which the predicted labels for the test set will be written.

       --probabilities_file (-p) [unknown]
              The matrix in which the predicted probability of labels for the test set will be written.

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.

mlpack-4.3.0                                     19 January 2024                                   mlpack_nbc(1)