| mlpack_adaboost(26 December 2016) | mlpack_adaboost(26 December 2016) |
NAME¶
mlpack_adaboost - adaboostSYNOPSIS¶
mlpack_adaboost [-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 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.
OPTIONAL INPUT 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 ''.
- --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'.
OPTIONAL OUTPUT OPTIONS¶
- --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 ''.