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mlpack_adaboost - adaboost


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


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.


--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'.



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


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