Meta classifiers – classifiers which use other classifiers or preprocessing

Meta Classifiers can be grouped according to their function as

group BoostedClassifiers:
 CombinedClassifier MulticlassClassifier SplitClassifier
group ProxyClassifiers:
 ProxyClassifier BinaryClassifier MappedClassifier FeatureSelectionClassifier
group PredictionsCombiners for CombinedClassifier:
 PredictionsCombiner MaximalVote MeanPrediction

Inheritance diagram of mvpa2.clfs.meta



BinaryClassifier(clf, poslabels, neglabels, ...) ProxyClassifier which maps set of two labels into +1 and -1
BoostedClassifier([clfs, propagate_ca]) Classifier containing the farm of other classifiers.
ClassifierCombiner(clf[, variables]) Provides a decision using training a classifier on predictions/estimates
CombinedClassifier([clfs, combiner]) BoostedClassifier which combines predictions using some
FeatureSelectionClassifier(clf, mapper, **kwargs) This is nothing but a MappedClassifier.
MappedClassifier(clf, mapper, **kwargs) ProxyClassifier which uses some mapper prior training/testing.
MaximalVote(**kwargs) Provides a decision using maximal vote rule
MeanPrediction([descr]) Provides a decision by taking mean of the results
MulticlassClassifier(clf[, bclf_type]) Perform multiclass classification using a list of binary classifiers.
PredictionsCombiner([descr]) Base class for combining decisions of multiple classifiers
ProxyClassifier(clf, **kwargs) Classifier which decorates another classifier
RegressionAsClassifier(clf[, centroids, ...]) Allows to use arbitrary regression for classification.
SplitClassifier(clf[, partitioner, splitter]) BoostedClassifier to work on splits of the data
TreeClassifier(clf, groups, **kwargs) TreeClassifier which allows to create hierarchy of classifiers