|
||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES | |||||||||
| 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. |
| ClassificationViaClustering | A simple meta-classifier that uses a clusterer for classification. |
| 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. |
| Dagging | This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. |
| Decorate | DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. |
| END | A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
| FilteredClassifier | Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. |
| Grading | Implements Grading. |
| GridSearch | Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). |
| LogitBoost | Class for performing additive logistic regression. |
| MetaCost | This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. |
| MultiBoostAB | Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. |
| MultiClassClassifier | 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. |
| OrdinalClassClassifier | Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. |
| RacedIncrementalLogitBoost | Classifier for incremental learning of large datasets by way of racing logit-boosted committees. For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets. |
| 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. |
| RotationForest | Class for construction a Rotation Forest. |
| Stacking | Combines several classifiers using the stacking method. |
| StackingC | Implements StackingC (more efficient version of stacking). For more information, see A.K. |
| ThresholdSelector | A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. |
| Vote | Class for combining classifiers. |
|
||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES | |||||||||