AdaBoostM1
Package
weka.classifiers.meta
Synopsis
Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits.
For more information, see
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
Options
The table below describes the options available for AdaBoostM1.
Option |
Description |
|---|---|
classifier |
The base classifier to be used. |
debug |
If set to true, classifier may output additional info to the console. |
numIterations |
The number of iterations to be performed. |
seed |
The random number seed to be used. |
useResampling |
Whether resampling is used instead of reweighting. |
weightThreshold |
Weight threshold for weight pruning. |
Capabilities
The table below describes the capabilites of AdaBoostM1.
Capability |
Supported |
|---|---|
Class |
Binary class, Nominal class, Missing class values |
Attributes |
Date attributes, Numeric attributes, Binary attributes, Nominal attributes, Unary attributes, Missing values, Empty nominal attributes |
Min # of instances |
1 |