NAME

       mlpack_adaboost - adaboost

SYNOPSIS

        mlpack_adaboost [-h] [-v]

DESCRIPTION

       This  program  implements  the  AdaBoost  (or Adaptive Boosting) algorithm. The variant of
       AdaBoost implemented here is AdaBoost.MH. It uses a weak learner, either  decision  stumps
       or  perceptrons,  and  over  many  iterations, creates a strong learner that is a weighted
       ensemble of weak learners. It runs these iterations until a tolerance value is crossed for
       change in the value of the weighted training error.

       For  more  information  about  the  algorithm, see the paper "Improved Boosting Algorithms
       Using Confidence-Rated Predictions", by R.E. Schapire and Y.  Singer.

       This program allows training of an AdaBoost model, and then application of that model to a
       test  dataset.  To  train  a model, a dataset must be passed with the --training_file (-t)
       option. Labels can be given with the --labels_file (-l)  option;  if  no  labels  file  is
       specified,  the  labels  will  be  assumed  to  be  the  last column of the input dataset.
       Alternately, an AdaBoost model may be loaded with the --input_model_file (-m) option.

       Once a model is trained or loaded, it may be used to provide class predictions for a given
       test  dataset.  A  test  dataset may be specified with the --test_file (-T) parameter. The
       predicted classes for each point in the test dataset will be saved into the file specified
       by  the  --output_file  (-o)  parameter.  The AdaBoost model itself may be saved to a file
       specified by the --output_model_file (-M) parameter.

OPTIONAL INPUT OPTIONS

       --help (-h)
              Default help info.

       --info [string]
              Get help on a specific module or option.   Default  value  ''.   --input_model_file
              (-m) [string] File containing input AdaBoost model. Default value ''.

       --iterations (-i) [int]
              The maximum number of boosting iterations to be run (0 will run until convergence.)
              Default value 1000.

       --labels_file (-l) [string]
              A file containing labels for the training set.  Default value ''.

       --test_file (-T) [string]
              A file containing the test set. Default value ’'.

       --tolerance (-e) [double]
              The tolerance for change in values of the weighted error during  training.  Default
              value  1e-10.   --training_file  (-t)  [string] A file containing the training set.
              Default value ''.

       --verbose (-v)
              Display informational messages and the full list of parameters and  timers  at  the
              end of execution.

       --version (-V)
              Display  the  version  of  mlpack.   --weak_learner  (-w) [string] The type of weak
              learner to use: ’decision_stump', or 'perceptron'. Default value 'decision_stump'.

OPTIONAL OUTPUT OPTIONS

       --output_file (-o) [string]
              The file in which the predicted labels for the test set will  be  written.  Default
              value  ''.   --output_model_file  (-M) [string] File to save trained AdaBoost model
              to. Default value ''.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

       For further information, including relevant papers, citations,  and  theory,  For  further
       information,  including  relevant papers, citations, and theory, consult the documentation
       found at http://www.mlpack.org or included with your consult the  documentation  found  at
       http://www.mlpack.org  or  included  with  your  DISTRIBUTION  OF MLPACK.  DISTRIBUTION OF
       MLPACK.

                                                                mlpack_adaboost(16 November 2017)