Multivariate Pattern Analysis in Python

### Navigation

- index
- modules |
- modules |
- PyMVPA Home |
- Sitemap »

# mvpa2.featsel.base

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).

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 Initialize with number of steps :Parameters: bestdetector : BestDetector used to determine where the best error is located.
`SensitivityBasedFeatureSelection`(...[, ...]) | Feature elimination.
`SliceMapper`(slicearg, **kwargs) | Baseclass of Mapper that slice a Dataset in various ways.
`StaticFeatureSelection`(slicearg[, dshape, ...]) | Feature selection by static slicing argument.

### Navigation

- index
- modules |
- modules |
- PyMVPA Home |
- Sitemap »