mvpa2.clfs.metaΒΆ
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 | |
Functions
asobjarray(x) |
Generates numpy.ndarray with dtype object from an iterable |
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 |
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 |
Classifier([space]) |
Abstract classifier class to be inherited by all classifiers |
ClassifierCombiner(clf[, variables]) |
Provides a decision using training a classifier on predictions/estimates |
CombinedClassifier([clfs, combiner]) |
BoostedClassifier which combines predictions using some |
ConditionalAttribute([enabled]) |
Simple container intended to conditionally store the value |
Dataset(samples[, sa, fa, a]) |
Generic storage class for datasets with multiple attributes. |
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 |
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. |
NFoldPartitioner([cvtype]) |
Generic N-fold data partitioner. |
Parameter(default[, constraints, ro, index, ...]) |
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 |
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 |
