mvpa2.featsel.rfeΒΆ
Recursive feature elimination.
Functions
| copy(x) | Shallow copy operation on arbitrary Python objects. |
| maxofabs_sample() | Returns a mapper that finds max of absolute values of all samples. |
| mean_mismatch_error(predicted, target) | Computes the percentage of mismatches between some target and some predicted values. |
Classes
| BestDetector([func, lastminimum]) | Determine whether the last value in a sequence is the best one given some criterion. |
| BinaryFxNode(fx, space, **kwargs) | Extract a dataset attribute and call a function with it and the samples. |
| ClassifierError(clf[, labels, train]) | Compute (or return) some error of a (trained) classifier on a dataset. |
| ConditionalAttribute([enabled]) | Simple container intended to conditionally store the value |
| FeatureSelectionClassifier(clf, mapper, **kwargs) | This is nothing but a MappedClassifier. |
| 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 |
| ProxyClassifier(clf, **kwargs) | Classifier which decorates another classifier |
| ProxyMeasure(measure[, skip_train]) | Wrapper to allow for alternative post-processing of a shared measure. |
| RFE(fmeasure, pmeasure, splitter[, ...]) | Recursive feature elimination. |
| Repeater(count[, space]) | Node that yields the same dataset for a certain number of repetitions. |
| Sensitivity(clf[, force_train]) | Sensitivities of features for a given Classifier. |
| SplitRFE(lrn, partitioner, fselector[, ...]) | RFE with the nested cross-validation to estimate optimal number of features. |
| Splitter(attr[, attr_values, count, ...]) | Generator node for dataset splitting. |
