NAME

       mlpack_adaboost - adaboost

SYNOPSIS

        mlpack_adaboost [-m unknown] [-i int] [-l string] [-T string] [-e double] [-t string] [-V bool] [-w string] [-o string] [-M unknown] [-P string] [-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  are
       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 are output to the '--predictions_file
       (-P)'  output  parameter.  The AdaBoost model itself is output to the '--output_model_file
       (-M)' output parameter.

       Note: the following  parameter  is  deprecated  and  will  be  removed  in  mlpack  4.0.0:
       '--output_file (-o)'.  Use '--predictions_file (-P)' instead of '--output_file (-o)'.

       For  example,  to run AdaBoost on an input dataset 'data.csv' with perceptrons as the weak
       learner type, storing the trained model  in  'model.bin',  one  could  use  the  following
       command:

       $  mlpack_adaboost  --training_file  data.csv --output_model_file model.bin --weak_learner
       perceptron

       Similarly,  an  already-trained  model  in  'model.bin'  can  be  used  to  provide  class
       predictions  from test data 'test_data.csv' and store the output in ’predictions.csv' with
       the following command:

       $    mlpack_adaboost    --input_model_file     model.bin     --test_file     test_data.csv
       --predictions_file predictions.csv

OPTIONAL INPUT OPTIONS

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

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

       --input_model_file (-m) [unknown]
              Input AdaBoost model.

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

       --labels_file (-l) [string]
              Labels for the training set.

       --test_file (-T) [string]
              Test dataset.

       --tolerance (-e) [double]
              The tolerance for change in values of the weighted error during  training.  Default
              value 1e-10.

       --training_file (-t) [string]
              Dataset for training AdaBoost.

       --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.

       --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]
              Predicted labels for the test set.

       --output_model_file (-M) [unknown]
              Output trained AdaBoost model.

       --predictions_file (-P) [string]
              Predicted labels for the test set.

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.