Package org.tribuo.classification.mnb

Class MultinomialNaiveBayesModel

java.lang.Object
org.tribuo.Model<Label>
org.tribuo.classification.mnb.MultinomialNaiveBayesModel
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>, Serializable
public class MultinomialNaiveBayesModel extends Model<Label>
A Model for multinomial Naive Bayes with Laplace smoothing.

All feature values must be non-negative, otherwise it will throw IllegalArgumentException.

See: 

 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.
See Also:

  • Method Details

    • predict

      public Prediction<Label> predict(Example<Label> example)
      Description copied from class: Model
      Uses the model to predict the output for a single example.

      predict does not mutate the example.

      Throws IllegalArgumentException if the example has no features or no feature overlap with the model.

      Specified by:
      predict in class Model<Label>
      Parameters:
      example - the example to predict.
      Returns:
      the result of the prediction.

    • getTopFeatures

      public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)
      Description copied from class: Model
      Gets the top n features 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 Collections.emptyMap().

      Specified by:
      getTopFeatures in class Model<Label>
      Parameters:
      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.
      Returns:
      a map from string outputs to an ordered list of pairs of feature names and weights associated with that feature in the model

    • getExcuse

      public Optional<Excuse<Label>> getExcuse(Example<Label> example)
      Description copied from class: Model
      Generates an excuse for an 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.

      Specified by:
      getExcuse in class Model<Label>
      Parameters:
      example - The input example.
      Returns:
      An optional excuse object. The optional is empty if this model does not provide excuses.

    • copy

      protected MultinomialNaiveBayesModel copy(String newName, ModelProvenance newProvenance)
      Description copied from class: Model
      Copies a model, replacing its provenance and name with the supplied values.

      Used to provide the provenance removal functionality.

      Specified by:
      copy in class Model<Label>
      Parameters:
      newName - The new name.
      newProvenance - The new provenance.
      Returns:
      A copy of the model.