nodes.learning

NaiveBayesEstimator

case class NaiveBayesEstimator[T <: Vector[Double]](numClasses: Int, lambda: Double = 1.0)(implicit evidence$1: ClassTag[T]) extends LabelEstimator[T, DenseVector[Double], Int] with Product with Serializable

A LabelEstimator which learns a multinomial naive bayes model from training data. Outputs a Transformer that maps features to vectors containing the log-posterior-probabilities of the various classes according to the learned model.

lambda

The lambda parameter to use for the naive bayes model

Linear Supertypes
Product, Equals, LabelEstimator[T, DenseVector[Double], Int], EstimatorNode, Serializable, Serializable, Node, AnyRef, Any
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  1. NaiveBayesEstimator
  2. Product
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  4. LabelEstimator
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  7. Serializable
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Instance Constructors

  1. new NaiveBayesEstimator(numClasses: Int, lambda: Double = 1.0)(implicit arg0: ClassTag[T])

    lambda

    The lambda parameter to use for the naive bayes model

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
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  4. final def ==(arg0: AnyRef): Boolean

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

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

    Definition Classes
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  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( ... )

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

    Definition Classes
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  9. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( classOf[java.lang.Throwable] )

  10. def fit(in: RDD[T], labels: RDD[Int]): NaiveBayesModel[T]

    A LabelEstimator estimator is an estimator which expects labeled data.

    A LabelEstimator estimator is an estimator which expects labeled data.

    labels

    Input labels.

    returns

    A Transformer which can be called on new data.

    Definition Classes
    NaiveBayesEstimator → LabelEstimator

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

    Definition Classes
    AnyRef → Any

  12. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  13. def label: String

    Definition Classes
    Node

  14. val lambda: Double

    The lambda parameter to use for the naive bayes model

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

    Definition Classes
    AnyRef

  16. final def notify(): Unit

    Definition Classes
    AnyRef

  17. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  18. val numClasses: Int

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

    Definition Classes
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  20. final def wait(): Unit

    Definition Classes
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    @throws( ... )

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

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    @throws( ... )

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

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    @throws( ... )

  23. def withData(data: RDD[T], labels: RDD[Int]): Pipeline[T, DenseVector[Double]]

    Constructs a pipeline from a single label estimator and training data.

    Constructs a pipeline from a single label estimator and training data. Equivalent to Pipeline() andThen (estimator, data, labels)

    data

    The training data

    labels

    The training labels

    Definition Classes
    LabelEstimator

Inherited from Product

Inherited from Equals

Inherited from LabelEstimator[T, DenseVector[Double], Int]

Inherited from EstimatorNode

Inherited from Serializable

Inherited from Serializable

Inherited from Node

Inherited from AnyRef

Inherited from Any

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