public class LibLinearRegressionModel extends LibLinearModel<Regressor>
Modelwhich wraps a LibLinear-java model.
It disables the LibLinear debug output as it's very chatty.
It contains an independent liblinear model for each regression dimension.
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.
|Modifier and Type||Method and Description|
Copies a model, replacing it's provenance and name with the supplied values.
Extracts the feature weights from the models.
Gets the top
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.
copyModel, getExcuse, getExcuses, getInnerModels
copy, generatesProbabilities, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, innerPredict, predict, predict, setName, toString, validate
public Prediction<Regressor> predict(Example<Regressor> example)
predict does not mutate the example.
IllegalArgumentException if the example has no features
or no feature overlap with the model.
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
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.
protected LibLinearRegressionModel copy(String newName, ModelProvenance newProvenance)
Used to provide the provenance removal functionality.
protected double getFeatureWeights()
If it becomes a problem then we could cache the feature weights in the model.
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