org.apache.spark.mllib.classification

NaiveBayes

class NaiveBayes extends Serializable with 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.

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@Since( "0.9.0" )
Source
NaiveBayes.scala
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Instance Constructors

  1. new NaiveBayes()

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    @Since( "0.9.0" )

  2. new NaiveBayes(lambda: Double)

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    @Since( "1.4.0" )

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  11. final def getClass(): Class[_]

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  12. def getLambda: Double

    Get the smoothing parameter.

    Get the smoothing parameter.

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    @Since( "1.4.0" )

  13. def getModelType: String

    Get the model type.

    Get the model type.

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  16. def isTraceEnabled(): Boolean

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  17. def log: Logger

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  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logDebug(msg: ⇒ String): Unit

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  20. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logError(msg: ⇒ String): Unit

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  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logInfo(msg: ⇒ String): Unit

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  24. def logName: String

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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  32. def run(data: RDD[LabeledPoint]): NaiveBayesModel

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

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

    data

    RDD of org.apache.spark.mllib.regression.LabeledPoint.

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    @Since( "0.9.0" )

  33. def setLambda(lambda: Double): NaiveBayes

    Set the smoothing parameter.

    Set the smoothing parameter. Default: 1.0.

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    @Since( "0.9.0" )

  34. def setModelType(modelType: String): NaiveBayes

    Set the model type using a string (case-sensitive).

    Set the model type using a string (case-sensitive). Supported options: "multinomial" (default) and "bernoulli".

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    @Since( "1.4.0" )

  35. final def synchronized[T0](arg0: ⇒ T0): T0

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  36. def toString(): String

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