public class LinearSGDModel extends Model<Regressor>
Bottou L. "Large-Scale Machine Learning with Stochastic Gradient Descent" Proceedings of COMPSTAT, 2010.
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.
Gets the top
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<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.
public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)
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.
public Optional<Excuse<Regressor>> getExcuse(Example<Regressor> 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.
protected LinearSGDModel copy(String newName, ModelProvenance newProvenance)
Used to provide the provenance removal functionality.
public DenseMatrix getWeightsCopy()
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