public class IndependentMultiLabelModel extends Model<MultiLabel>
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.
ALL_OUTPUTS, BIAS_FEATURE, featureIDMap, generatesProbabilities, name, outputIDInfo, provenance, provenanceOutput
|Modifier and Type||Method and Description|
Copies a model, replacing it's provenance and name with the supplied values.
Generates an excuse for an example.
This aggregates the top features from each of the models.
Uses the model to predict the output for a single example.
copy, generatesProbabilities, getExcuses, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, innerPredict, predict, predict, setName, toString, validate
public Prediction<MultiLabel> predict(Example<MultiLabel> example)
predict does not mutate the example.
IllegalArgumentException if the example has no features
or no feature overlap with the model.
example- the example to predict.
public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)
If the individual models support per label features, then only the features for the positive label are aggregated.
n- the number of features to return. If this value is less than 0, all features should be returned for each class.
public Optional<Excuse<MultiLabel>> getExcuse(Example<MultiLabel> 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.
example- The input example.
protected IndependentMultiLabelModel copy(String newName, ModelProvenance newProvenance)
Used to provide the provenance removal functionality.
newName- The new name.
newProvenance- The new provenance.
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