org.tribuo.classification.mnb

Class MultinomialNaiveBayesTrainer

java.lang.Object

org.tribuo.classification.mnb.MultinomialNaiveBayesTrainer

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<Label>, WeightedExamples

public class MultinomialNaiveBayesTrainer
extends Object
implements Trainer<Label>, WeightedExamples

A Trainer which trains a multinomial Naive Bayes model with Laplace smoothing.

All feature values must be non-negative.

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.
 

Field Summary

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Constructor and Description
MultinomialNaiveBayesTrainer()
MultinomialNaiveBayesTrainer(double alpha)

Method Summary

Modifier and Type	Method and Description
int	getInvocationCount() The number of times this trainer instance has had it's train method invoked.
TrainerProvenance	getProvenance()
String	toString()
Model<Label>	train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a predictive model using the examples in the given data set.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.tribuo.Trainer

train

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

Constructor Detail

MultinomialNaiveBayesTrainer

public MultinomialNaiveBayesTrainer()

MultinomialNaiveBayesTrainer

public MultinomialNaiveBayesTrainer(double alpha)

Method Detail

train

public Model<Label> train(Dataset<Label> examples,
                          Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Description copied from interface: Trainer

Trains a predictive model using the examples in the given data set.

Specified by:

train in interface Trainer<Label>

Parameters:

examples - the data set containing the examples.

runProvenance - Training run specific provenance (e.g., fold number).

Returns:

a predictive model that can be used to generate predictions for new examples.

getInvocationCount

public int getInvocationCount()

Description copied from interface: Trainer

The number of times this trainer instance has had it's train method invoked.

This is used to determine how many times the trainer's RNG has been accessed to ensure replicability in the random number stream.

Specified by:

getInvocationCount in interface Trainer<Label>

Returns:

The number of train invocations.

toString

public String toString()

Overrides:

toString in class Object

getProvenance

public TrainerProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>