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Multivariate Pattern Analysis in Python |
Inheritance diagram for mvpa.featsel.base:
Feature selection base class and related stuff base classes and helpers.
Bases: mvpa.featsel.base.FeatureSelection
Meta feature selection utilizing several embedded selection methods.
Each embedded feature selection method is computed individually. Afterwards all feature sets are combined by either taking the union or intersection of all sets.
The individual feature sets of all embedded methods are optionally avialable from the selections_ids state variable.
Note
Available state variables:
(States enabled by default are listed with +)
Parameters: |
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Bases: mvpa.misc.state.ClassWithCollections
Base class for any feature selection
Base class for Functors which implement feature selection on the datasets.
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Initialize instance of FeatureSelection
Parameters: |
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‘Untrain’ feature selection
Necessary for full ‘untraining’ of the classifiers. By default does nothing, needs to be overridden in corresponding feature selections to pass to the sensitivities
Bases: mvpa.featsel.base.FeatureSelection
Feature elimination through the list of FeatureSelection’s.
Given as list of FeatureSelections it applies them in turn.
Note
Available state variables:
(States enabled by default are listed with +)
Initialize feature selection pipeline
Parameters: |
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Bases: mvpa.featsel.base.FeatureSelection
Feature elimination.
A FeaturewiseDatasetMeasure is used to compute sensitivity maps given a certain dataset. These sensitivity maps are in turn used to discard unimportant features.
Note
Available state variables:
(States enabled by default are listed with +)
Initialize feature selection
Parameters: |
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