Multivariate Pattern Analysis in Python

# mvpa2.clfs.meta

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

Classes

`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