NAME

       mlpack_adaboost - adaboost

SYNOPSIS

        mlpack_adaboost [-m unknown] [-i int] [-l unknown] [-T unknown] [-e double] [-t unknown] [-V bool] [-w string] [-o unknown] [-M unknown] [-P unknown] [-p unknown] [-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 labels ’labels.csv'and 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  --labels_file  labels.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) [unknown] Labels for the training set.

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

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

       --training_file (-t) [unknown]
              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) [unknown] Predicted labels for the test set.

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

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

       --probabilities_file (-p) [unknown]
              Predicted class probabilities for each point in 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.

mlpack-4.3.0                                     19 January 2024                              mlpack_adaboost(1)