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Multivariate Pattern Analysis in Python |
Inheritance diagram for mvpa.featsel.ifs:
Incremental feature search (IFS).
Very similar to Recursive feature elimination (RFE), but instead of begining with all features and stripping some sequentially, start with an empty feature set and include important features successively.
Bases: mvpa.featsel.base.FeatureSelection
Incremental feature search.
A scalar DatasetMeasure is computed multiple times on variations of a certain dataset. These measures are in turn used to incrementally select important features. Starting with an empty feature set the dataset measure is first computed for each single feature. A number of features is selected based on the resulting data measure map (using an ElementSelector).
Next the dataset measure is computed again using each feature in addition to the already selected feature set. Again the ElementSelector is used to select more features.
For each feature selection the transfer error on some testdatset is computed. This procedure is repeated until a given StoppingCriterion is reached.
Note
Available state variables:
(States enabled by default are listed with +)
Initialize incremental feature search
Parameters: |
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