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)