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.

Annotations
@Since( "0.9.0" )
Source
NaiveBayes.scala
Linear Supertypes
Logging, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. NaiveBayes
  2. Logging
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new NaiveBayes()

    Annotations
    @Since( "0.9.0" )
  2. new NaiveBayes(lambda: Double)

    Annotations
    @Since( "1.4.0" )

Value Members

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

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  12. def getLambda: Double

    Get the smoothing parameter.

    Get the smoothing parameter.

    Annotations
    @Since( "1.4.0" )
  13. def getModelType: String

    Get the model type.

    Get the model type.

    Annotations
    @Since( "1.4.0" )
  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  16. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  17. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  19. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  20. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  21. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  23. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  24. def logName: String

    Attributes
    protected
    Definition Classes
    Logging
  25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  26. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  28. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  29. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  30. final def notify(): Unit

    Definition Classes
    AnyRef
  31. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  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.

    Annotations
    @Since( "0.9.0" )
  33. def setLambda(lambda: Double): NaiveBayes

    Set the smoothing parameter.

    Set the smoothing parameter. Default: 1.0.

    Annotations
    @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".

    Annotations
    @Since( "1.4.0" )
  35. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  36. def toString(): String

    Definition Classes
    AnyRef → Any
  37. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  38. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped