- All Implemented Interfaces:
Modelwhich wraps n binary models, where n is the size of the MultiLabel domain. Each model independently predicts a single binary label.
It is possible for the prediction to produce an empty MultiLabel when none of the binary Labels were predicted.
This model implements the approach known as "Binary Relevance" in the multi-label classification literature.
- See Also:
Method SummaryModifier and TypeMethodDescription
protected IndependentMultiLabelModelCopies a model, replacing its provenance and name with the supplied values.Generates an excuse for an example.
(int n)This aggregates the top features from each of the models.Uses the model to predict the output for a single example.
Methods inherited from class org.tribuo.Model
castModel, copy, generatesProbabilities, getExcuses, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, innerPredict, predict, predict, setName, toString, validate
predictUses the model to predict the output for a single example.
predict does not mutate the example.
IllegalArgumentExceptionif the example has no features or no feature overlap with the model.
getTopFeaturesThis aggregates the top features from each of the models.
If the individual models support per label features, then only the features for the positive label are aggregated.
getExcuseGenerates an excuse for an example.
This attempts to explain a classification result. Generating an excuse may be quite an expensive operation.
This excuse either contains per class information or an entry with key Model.ALL_OUTPUTS.
The optional is empty if the model does not provide excuses.
copyCopies a model, replacing its provenance and name with the supplied values.
Used to provide the provenance removal functionality.