|
Class Summary |
| 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.
For more information, see:
R. |
| FilteredClassifier |
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. |
| 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. |
| 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. |