org.apache.spark.mllib.classification

NaiveBayes

object NaiveBayes extends Serializable

Top-level methods for calling naive Bayes.

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

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 hashCode(): Int

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

    Definition Classes
    Any
  14. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  17. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  18. def toString(): String

    Definition Classes
    AnyRef → Any
  19. def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel

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

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

    The model type can be set to either Multinomial NB (http://tinyurl.com/lsdw6p) or Bernoulli NB (http://tinyurl.com/p7c96j6). The Multinomial NB can handle discrete count data and can be called by setting the model type to "multinomial". For example, it can be used with word counts or TF_IDF vectors of documents. The Bernoulli model fits presence or absence (0-1) counts. By making every vector a 0-1 vector and setting the model type to "bernoulli", the fits and predicts as Bernoulli NB.

    input

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

    lambda

    The smoothing parameter

    modelType

    The type of NB model to fit from the enumeration NaiveBayesModels, can be multinomial or bernoulli

    Annotations
    @Since( "1.4.0" )
  20. def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel

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

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

    This is the default 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.

    input

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

    lambda

    The smoothing parameter

    Annotations
    @Since( "0.9.0" )
  21. def train(input: RDD[LabeledPoint]): NaiveBayesModel

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

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

    This is the default 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.

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

    input

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

    Annotations
    @Since( "0.9.0" )
  22. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped