|
Class Summary |
| AdaBoostM1 |
Class for boosting a classifier using Freund & Schapire's Adaboost
M1 method. |
| AdditiveRegression |
Meta classifier that enhances the performance of a regression base
classifier. |
| AttributeSelectedClassifier |
Class for running an arbitrary classifier on data that has been reduced
through attribute selection. |
| Bagging |
Class for bagging a classifier. |
| ClassificationViaRegression |
Class for doing classification using regression methods. |
| CostSensitiveClassifier |
This metaclassifier makes its base classifier cost-sensitive. |
| CVParameterSelection |
Class for performing parameter selection by cross-validation for any
classifier. |
| Decorate |
DECORATE is a meta-learner for building diverse ensembles of
classifiers by using specially constructed artificial training
examples. |
| END |
Class for creating a committee of random classifiers. |
| FilteredClassifier |
Class for running an arbitrary classifier on data that has been passed
through an arbitrary filter. |
| Grading |
Implements Grading. |
| HND |
Class to create levelwise NDs with respect to a given hierarchy of classes.
|
| LogitBoost |
Class for performing additive logistic regression..
|
| MetaCost |
This metaclassifier makes its base classifier cost-sensitive using the
method specified in
Pedro Domingos (1999). |
| MultiBoostAB |
Class for boosting a classifier using the MultiBoosting method. |
| MultiClassClassifier |
Class for handling multi-class datasets with 2-class distribution
classifiers. |
| MultiScheme |
Class for selecting a classifier from among several using cross
validation on the training data or the performance on the
training data. |
| ND |
|
| OrdinalClassClassifier |
Meta classifier for transforming an ordinal class problem to a series
of binary class problems. |
| RacedIncrementalLogitBoost |
Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
|
| RandomCommittee |
Class for creating a committee of random classifiers. |
| RegressionByDiscretization |
Class for a regression scheme that employs any distribution
classifier on a copy of the data that has the class attribute (equal-width)
discretized. |
| Stacking |
Implements stacking. |
| StackingC |
Implements StackingC (more efficient version of stacking). |
| ThresholdSelector |
Class for selecting a threshold on a probability output by a
distribution classifier. |
| TreeBasedMultiClassClassifier |
Class that represents and builds a classifier tree. |
| Vote |
Class for combining classifiers using unweighted average of
probability estimates (classification) or numeric predictions
(regression).
|