- All Implemented Interfaces:
Modelwhich wraps a LibLinear-java classification model.
It disables the LibLinear debug output as it's very chatty.
Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ. "LIBLINEAR: A library for Large Linear Classification" Journal of Machine Learning Research, 2008.and for the original algorithm:
Cortes C, Vapnik V. "Support-Vector Networks" Machine Learning, 1995.
- See Also:
Fields inherited from class org.tribuo.Model
ALL_OUTPUTS, BIAS_FEATURE, featureIDMap, generatesProbabilities, name, outputIDInfo, provenance, provenanceOutput
Method SummaryModifier and TypeMethodDescription
protected LibLinearClassificationModelCopies a model, replacing its provenance and name with the supplied values.
protected doubleExtracts the feature weights from the models.
(int n)Gets the top
nfeatures associated with this model.The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.Uses the model to predict the output for a single example.
Methods inherited from class org.tribuo.common.liblinear.LibLinearModel
copyModel, getExcuse, getExcuses, getInnerModels
Methods inherited from class org.tribuo.Model
castModel, copy, generatesProbabilities, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, innerPredict, predict, predict, setName, toString, validate
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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.
getTopFeaturesGets the top
nfeatures associated with this model.
If the model does not produce per output feature lists, it returns a map with a single element with key Model.ALL_OUTPUTS.
If the model cannot describe it's top features then it returns
- Specified by:
n- the number of features to return. If this value is less than 0, all features should be returned for each class, unless the model cannot score it's features.
- a map from string outputs to an ordered list of pairs of feature names and weights associated with that feature in the model
copyCopies a model, replacing its provenance and name with the supplied values.
Used to provide the provenance removal functionality.
getFeatureWeightsprotected double getFeatureWeights()Description copied from class:
LibLinearModelExtracts the feature weights from the models. The first dimension corresponds to the model index.
innerGetExcuseThe call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.
If it becomes a problem then we could cache the feature weights in the model.
exportONNXModelDescription copied from interface:
writeONNXGraphDescription copied from interface: