Meta classifiers – classifiers which use other classifiers or preprocessing
Meta Classifiers can be grouped according to their function as
group BoostedClassifiers: | |
---|---|
CombinedClassifier MulticlassClassifier SplitClassifier | |
group ProxyClassifiers: | |
ProxyClassifier BinaryClassifier MappedClassifier FeatureSelectionClassifier | |
group PredictionsCombiners for CombinedClassifier: | |
PredictionsCombiner MaximalVote MeanPrediction |
Functions
asobjarray(x) | Generates numpy.ndarray with dtype object from an iterable |
cartesian_distance(a, b) | Return Cartesian distance between a and b |
first_axis_mean(x) | Mean computed along the first axis. |
get_samples_by_attr(dataset, attr, values[, ...]) | Return indices of samples given a list of attributes |
group_kwargs(prefixes[, assign, passthrough]) | Decorator function to join parts of kwargs together |
is_sequence_type | isSequenceType(a) – Return True if a has a sequence type, False otherwise. |
Classes
AttributeMap([map, mapnumeric, ...]) | Map to translate literal values to numeric ones (and back). |
BinaryClassifier(clf, poslabels, neglabels, ...) | ProxyClassifier which maps set of two labels into +1 and -1 |
BinaryClassifierSensitivityAnalyzer(*args_, ...) | Set sensitivity analyzer output to have proper labels .. |
BoostedClassifier([clfs, propagate_ca]) | Classifier containing the farm of other classifiers. |
BoostedClassifierSensitivityAnalyzer(*args_, ...) | Set sensitivity analyzers to be merged into a single output .. |
ClassWithCollections([descr]) | Base class for objects which contain any known collection |
Classifier([space]) | Abstract classifier class to be inherited by all classifiers .. |
ClassifierCombiner(clf[, variables]) | Provides a decision using training a classifier on predictions/estimates |
CombinedClassifier([clfs, combiner]) | BoostedClassifier which combines predictions using some |
ConditionalAttribute([enabled]) | Simple container intended to conditionally store the value .. |
Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
FeatureSelectionClassifier(clf, mapper, **kwargs) | This is nothing but a MappedClassifier. |
FeatureSelectionClassifierSensitivityAnalyzer(...) | Notes |
MappedClassifier(clf, mapper, **kwargs) | ProxyClassifier which uses some mapper prior training/testing. |
MappedClassifierSensitivityAnalyzer(*args_, ...) | Set sensitivity analyzer output be reverse mapped using mapper of the slave classifier .. |
MaximalVote(**kwargs) | Provides a decision using maximal vote rule .. |
MeanPrediction([descr]) | Provides a decision by taking mean of the results .. |
MulticlassClassifier(clf[, bclf_type]) | Perform multiclass classification using a list of binary classifiers. |
NFoldPartitioner([cvtype]) | Generic N-fold data partitioner. |
Parameter(default[, constraints, ro, index, ...]) | This class shall serve as a representation of a parameter. |
PredictionsCombiner([descr]) | Base class for combining decisions of multiple classifiers .. |
ProxyClassifier(clf, **kwargs) | Classifier which decorates another classifier |
ProxyClassifierSensitivityAnalyzer(*args_, ...) | Set sensitivity analyzer output just to pass through .. |
RegressionAsClassifier(clf[, centroids, ...]) | Allows to use arbitrary regression for classification. |
RegressionAsClassifierSensitivityAnalyzer(...) | Set sensitivity analyzer output to have proper labels .. |
SplitClassifier(clf[, partitioner, splitter]) | BoostedClassifier to work on splits of the data |
Splitter(attr[, attr_values, count, ...]) | Generator node for dataset splitting. |
TreeClassifier(clf, groups, **kwargs) | TreeClassifier which allows to create hierarchy of classifiers |