Class/Object

org.apache.spark.mllib.classification

NaiveBayes

Related Docs: object NaiveBayes | package classification

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class NaiveBayes extends Serializable with Logging

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

This is the Multinomial NB (see here) 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 (see here). The input feature values must be nonnegative.

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@Since( "0.9.0" )
Source
NaiveBayes.scala
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Logging, Serializable, Serializable, AnyRef, Any
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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" )

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. def getLambda: Double

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    Get the smoothing parameter.

    Get the smoothing parameter.

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    @Since( "1.4.0" )

  11. def getModelType: String

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    Get the model type.

    Get the model type.

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    @Since( "1.4.0" )

  12. def hashCode(): Int

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  13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  14. final def isInstanceOf[T0]: Boolean

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  15. def isTraceEnabled(): Boolean

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  16. def log: Logger

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

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

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

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

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

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

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  23. def logName: String

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  24. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  25. def logTrace(msg: ⇒ String): Unit

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  26. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  27. def logWarning(msg: ⇒ String): Unit

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  28. final def ne(arg0: AnyRef): Boolean

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  29. final def notify(): Unit

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  30. final def notifyAll(): Unit

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

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    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.

    Annotations
    @Since( "0.9.0" )

  32. def setLambda(lambda: Double): NaiveBayes

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    Set the smoothing parameter.

    Set the smoothing parameter. Default: 1.0.

    Annotations
    @Since( "0.9.0" )

  33. def setModelType(modelType: String): NaiveBayes

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

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

    Annotations
    @Since( "1.4.0" )

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

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  35. def toString(): String

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  36. final def wait(): Unit

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  37. final def wait(arg0: Long, arg1: Int): Unit

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  38. final def wait(arg0: Long): Unit

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Inherited from Logging

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