Feature selection base class and related stuff base classes and helpers.

Inheritance diagram of mvpa2.featsel.base


accepts_dataset_as_samples(fx) Decorator to extract samples from Datasets.
mask2slice(mask) Convert a boolean mask vector into an equivalent slice (if possible).
split_by_sample_attribute(ds, sa_label[, ...]) Splits a dataset based on unique values of a sample attribute
vstack(datasets[, a, fa]) Stacks datasets vertically (appending samples).


BestDetector([func, lastminimum]) Determine whether the last value in a sequence is the best one given some criterion.
CombinedFeatureSelection(selectors, method, ...) Meta feature selection utilizing several embedded selection methods.
ConditionalAttribute([enabled]) Simple container intended to conditionally store the value
FeatureSelection([filler]) Mapper to select a subset of features.
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
SensitivityBasedFeatureSelection(...[, ...]) Feature elimination.
SliceMapper(slicearg, \*\*kwargs) Baseclass of Mapper that slice a Dataset in various ways.
SplitSamplesProbabilityMapper(...[, ...]) Mapper to select features & samples based on some sensitivity value.
StaticFeatureSelection(slicearg[, dshape, ...]) Feature selection by static slicing argument.