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mvpa.clfs.metaΒΆ
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 |
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(**kwargs[, clfs, ...]) | Classifier containing the farm of other classifiers. |
| BoostedClassifierSensitivityAnalyzer(*args_, ...) | Set sensitivity analyzers to be merged into a single output .. |
| ClassWithCollections(**kwargs[, descr]) | Base class for objects which contain any known collection |
| Classifier(**kwargs[, space]) | Abstract classifier class to be inherited by all classifiers .. |
| ClassifierCombiner(clf[, variables]) | Provides a decision using training a classifier on predictions/estimates |
| CombinedClassifier(**kwargs[, clfs, combiner]) | BoostedClassifier which combines predictions using some |
| ConditionalAttribute(*args, **kwargs[, enabled]) | Simple container intended to conditionally store the value |
| FeatureSelectionClassifier(clf, mapper, **kwargs) | This is nothing but a MappedClassifier. |
| FeatureSelectionClassifierSensitivityAnalyzer(...) | Notes |
| Harvestable(**kwargs[, harvest_attribs, ...]) | Classes inherited from this class intend to collect attributes within internal processing. |
| 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() | Provides a decision using maximal vote rule .. |
| MeanPrediction(**kwargs[, descr]) | Provides a decision by taking mean of the results .. |
| MulticlassClassifier(clf, **kwargs[, bclf_type]) | CombinedClassifier to perform multiclass using a list of |
| NFoldPartitioner(**kwargs[, cvtype]) | Generic N-fold data partitioner. |
| Parameter(default, **kwargs[, ro, index, ...]) | This class shall serve as a representation of a parameter. |
| PredictionsCombiner(**kwargs[, descr]) | Base class for combining decisions of multiple classifiers |
| ProxyClassifier(clf, **kwargs) | Classifier which decorates another classifier |
| ProxyClassifierSensitivityAnalyzer(*args_, ...) | Set sensitivity analyzer output just to pass through .. |
| RegressionAsClassifier(clf, **kwargs[, ...]) | Allows to use arbitrary regression for classification. |
| RegressionAsClassifierSensitivityAnalyzer(...) | Set sensitivity analyzer output to have proper labels .. |
| SplitClassifier(clf, **kwargs[, ...]) | BoostedClassifier to work on splits of the data |
| Splitter(attr, **kwargs[, attr_values, ...]) | Generator node for dataset splitting. |
| TreeClassifier(clf, groups, **kwargs) | TreeClassifier which allows to create hierarchy of classifiers |
