Modelfor multinomial Naive Bayes with Laplace smoothing.
All feature values must be non-negative, otherwise it will throw IllegalArgumentException.
Wang S, Manning CD. "Baselines and Bigrams: Simple, Good Sentiment and Topic Classification" Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 2012.
|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<Label> predict(Example<Label> 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.
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 MultinomialNaiveBayesModel copy(String newName, ModelProvenance newProvenance)
Used to provide the provenance removal functionality.
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