mlpack_adaboost man page

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'.

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 or included with your consult the documentation found at or included with your DISTRIBUTION OF MLPACK. DISTRIBUTION OF MLPACK.