NAME

       mlpack_adaboost - adaboost

SYNOPSIS

        mlpack_adaboost [-h] [-v] [-m string] [-i int] [-l string] [-o string] [-M string] [-T string] [-e double] [-t string] [-V] [-w string]

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.

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 ''.

       --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 ''.

       --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'.

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(1)