Recursive feature elimination.
Functions
BibTeX(*args, **kwargs) | Perform no good and no bad |
Doi(*args, **kwargs) | Perform no good and no bad |
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. |