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)