Multivariate Pattern Analysis in Python

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# mvpa2.clfs.meta

Classes for meta classifiers – classifiers which use other classifiers

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

Functions

`cartesian_distance`(a, b) | Return Cartesian distance between a and b
`first_axis_mean`(x) | Mean computed along the first axis.
`get_samples_by_attr`(dataset, attr, values[, ...]) | Return indices of samples given a list of attributes
`group_kwargs`(prefixes[, assign, passthrough]) | Decorator function to join parts of kwargs together :Parameters: prefixes : list of strs Prefixes to split based on.
`is_sequence_type` | isSequenceType(a) – Return True if a has a sequence type, False otherwise.

Classes

`AttributeMap`([map, mapnumeric, ...]) | Map to translate literal values to numeric ones (and back).
`BinaryClassifier`(clf, poslabels, neglabels, ...) | `ProxyClassifier` which maps set of two labels into +1 and -1
`BinaryClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output to have proper labels ..
`BoostedClassifier`([clfs, propagate_ca]) | Classifier containing the farm of other classifiers.
`BoostedClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzers to be merged into a single output ..
`ClassWithCollections`([descr]) | Base class for objects which contain any known collection Classes inherited from this class gain ability to access collections and their items as simple attributes.
`Classifier`([space]) | Abstract classifier class to be inherited by all classifiers ..
`ClassifierCombiner`(clf[, variables]) | Provides a decision using training a classifier on predictions/estimates TODO: implement ..
`CombinedClassifier`([clfs, combiner]) | `BoostedClassifier` which combines predictions using some
`ConditionalAttribute`([enabled]) | Simple container intended to conditionally store the value ..
`FeatureSelectionClassifier`(clf, mapper, **kwargs) | This is nothing but a `MappedClassifier`.
`FeatureSelectionClassifierSensitivityAnalyzer`(...) | Notes
`MappedClassifier`(clf, mapper, **kwargs) | `ProxyClassifier` which uses some mapper prior training/testing.
`MappedClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output be reverse mapped using mapper of the slave classifier ..
`MaximalVote`(**kwargs) | Provides a decision using maximal vote rule ..
`MeanPrediction`([descr]) | Provides a decision by taking mean of the results ..
`MulticlassClassifier`(clf[, bclf_type]) | `CombinedClassifier` to perform multiclass using a list of
`NFoldPartitioner`([cvtype]) | Generic N-fold data partitioner.
`Parameter`(default[, ro, index, value, name, doc]) | This class shall serve as a representation of a parameter.
`PredictionsCombiner`([descr]) | Base class for combining decisions of multiple classifiers ..
`ProxyClassifier`(clf, **kwargs) | Classifier which decorates another classifier Possible uses: - modify data somehow prior training/testing: * normalization * feature selection * modification - optimized classifier? ..
`ProxyClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output just to pass through ..
`RegressionAsClassifier`(clf[, centroids, ...]) | Allows to use arbitrary regression for classification.
`RegressionAsClassifierSensitivityAnalyzer`(...) | Set sensitivity analyzer output to have proper labels ..
`SplitClassifier`(clf[, partitioner, splitter]) | `BoostedClassifier` to work on splits of the data
`Splitter`(attr[, attr_values, count, ...]) | Generator node for dataset splitting.
`TreeClassifier`(clf, groups, **kwargs) | `TreeClassifier` which allows to create hierarchy of classifiers

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