| Class | Description |
|---|---|
| AdaBoostM1 |
Class for boosting a nominal class classifier using the Adaboost M1
method.
|
| AdditiveRegression |
Meta classifier that enhances the performance of a regression base
classifier.
|
| AttributeSelectedClassifier |
Dimensionality of training and test data is reduced by attribute
selection before being passed on to a classifier.
|
| Bagging |
Class for bagging a classifier to reduce variance.
|
| ClassificationViaRegression |
Class for doing classification using regression methods.
|
| CostSensitiveClassifier |
A metaclassifier that makes its base classifier cost-sensitive.
|
| CVParameterSelection |
Class for performing parameter selection by cross-validation for any classifier.
|
| FilteredClassifier |
Class for running an arbitrary classifier on data
that has been passed through an arbitrary filter.
|
| IterativeClassifierOptimizer |
Chooses the best number of iterations for an IterativeClassifier such as
LogitBoost using cross-validation or a percentage split evaluation
|
| LogitBoost |
Class for performing additive logistic regression.
|
| MultiClassClassifier |
A metaclassifier for handling multi-class datasets with 2-class
classifiers.
|
| MultiClassClassifierUpdateable |
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
| MultiScheme |
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
|
| RandomCommittee |
Class for building an ensemble of randomizable base classifiers.
|
| RandomizableFilteredClassifier |
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
|
| RandomSubSpace |
This method constructs a decision tree based classifier that maintains
highest accuracy on training data and improves on generalization accuracy as it grows in
complexity.
|
| RegressionByDiscretization |
A regression scheme that employs any classifier on a copy of the data
that has the class attribute (equal-width) discretized.
|
| Stacking |
Combines several classifiers using the stacking method.
|
| Vote |
Class for combining classifiers.
|
| WeightedInstancesHandlerWrapper |
Generic wrapper around any classifier to enable weighted instances support.
|