org.apache.spark.mllib.classification

Class NaiveBayes

Object

org.apache.spark.mllib.classification.NaiveBayes

All Implemented Interfaces:

java.io.Serializable, Logging

public class NaiveBayes
extends Object
implements scala.Serializable, Logging

Trains a Naive Bayes model given an RDD of (label, features) pairs.

This is the Multinomial NB (http://tinyurl.com/lsdw6p) which can handle all kinds of discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (http://tinyurl.com/p7c96j6). The input feature values must be nonnegative.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NaiveBayes()

Method Summary

Methods

Modifier and Type	Method and Description
NaiveBayesModel	run(RDD<LabeledPoint> data) Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
NaiveBayes	setLambda(double lambda) Set the smoothing parameter.
static NaiveBayesModel	train(RDD<LabeledPoint> input) Trains a Naive Bayes model given an RDD of (label, features) pairs.
static NaiveBayesModel	train(RDD<LabeledPoint> input, double lambda) Trains a Naive Bayes model given an RDD of (label, features) pairs.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

NaiveBayes

public NaiveBayes()

Method Detail

train

public static NaiveBayesModel train(RDD<LabeledPoint> input)

Trains a Naive Bayes model given an RDD of (label, features) pairs.

This is the Multinomial NB (http://tinyurl.com/lsdw6p) which can handle all kinds of discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (http://tinyurl.com/p7c96j6).

This version of the method uses a default smoothing parameter of 1.0.

Parameters:

input - RDD of (label, array of features) pairs. Every vector should be a frequency vector or a count vector.

train

public static NaiveBayesModel train(RDD<LabeledPoint> input,
                    double lambda)

Trains a Naive Bayes model given an RDD of (label, features) pairs.

This is the Multinomial NB (http://tinyurl.com/lsdw6p) which can handle all kinds of discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (http://tinyurl.com/p7c96j6).

Parameters:

input - RDD of (label, array of features) pairs. Every vector should be a frequency vector or a count vector.

lambda - The smoothing parameter

setLambda

public NaiveBayes setLambda(double lambda)

Set the smoothing parameter. Default: 1.0.

run

public NaiveBayesModel run(RDD<LabeledPoint> data)

Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

Parameters:

data - RDD of LabeledPoint.