Recursive feature elimination.

Inheritance diagram of mvpa2.featsel.rfe


BibTeX(\*args, \*\*kwargs) Perform no good and no bad
Doi(\*args, \*\*kwargs) Perform no good and no bad
copy(x) Shallow copy operation on arbitrary Python objects.
maxofabs_sample() Returns a mapper that finds max of absolute values of all samples.
mean_mismatch_error(predicted, target) Computes the percentage of mismatches between some target and some predicted values.


BestDetector([func, lastminimum]) Determine whether the last value in a sequence is the best one given some criterion.
BinaryFxNode(fx, space, \*\*kwargs) Extract a dataset attribute and call a function with it and the samples.
ClassifierError(clf[, labels, train]) Compute (or return) some error of a (trained) classifier on a dataset.
ConditionalAttribute([enabled]) Simple container intended to conditionally store the value
FeatureSelectionClassifier(clf, mapper, \*\*kwargs) This is nothing but a MappedClassifier.
FractionTailSelector(felements, \*\*kwargs) Given a sequence, provide Ids for a fraction of elements
IterativeFeatureSelection(fmeasure, ...[, ...])


NBackHistoryStopCrit([bestdetector, steps]) Stop computation if for a number of steps error was increasing
ProxyClassifier(clf, \*\*kwargs) Classifier which decorates another classifier
ProxyMeasure(measure[, skip_train]) Wrapper to allow for alternative post-processing of a shared measure.
RFE(fmeasure, pmeasure, splitter[, ...]) Recursive feature elimination.
Repeater(count[, space]) Node that yields the same dataset for a certain number of repetitions.
Sensitivity(clf[, force_train]) Sensitivities of features for a given Classifier.
SplitRFE(lrn, partitioner, fselector[, ...]) RFE with the nested cross-validation to estimate optimal number of features.
Splitter(attr[, attr_values, count, ...]) Generator node for dataset splitting.