Feature selection base class and related stuff base classes and helpers.
Functions
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). |
Classes
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, ...[, ...]) | Notes |
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. |