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.