org.apache.spark.mllib.evaluation

MultilabelMetrics

class MultilabelMetrics extends AnyRef

Evaluator for multilabel classification.

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. MultilabelMetrics
  2. AnyRef
  3. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])])

    predictionAndLabels

    an RDD of (predictions, labels) pairs, both are non-null Arrays, each with unique elements.

Value Members

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

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef → Any

  4. lazy val accuracy: Double

    Returns accuracy

  5. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  6. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef

  8. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  9. def f1Measure(label: Double): Double

    Returns f1-measure for a given label (category)

    Returns f1-measure for a given label (category)

    label

    the label.

  10. lazy val f1Measure: Double

    Returns document-based f1-measure averaged by the number of documents

  11. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  13. lazy val hammingLoss: Double

    Returns Hamming-loss

  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  16. lazy val labels: Array[Double]

    Returns the sequence of labels in ascending order

  17. lazy val microF1Measure: Double

    Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)

  18. lazy val microPrecision: Double

    Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)

  19. lazy val microRecall: Double

    Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)

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

    Definition Classes
    AnyRef

  21. final def notify(): Unit

    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  23. def precision(label: Double): Double

    Returns precision for a given label (category)

    Returns precision for a given label (category)

    label

    the label.

  24. lazy val precision: Double

    Returns document-based precision averaged by the number of documents

  25. def recall(label: Double): Double

    Returns recall for a given label (category)

    Returns recall for a given label (category)

    label

    the label.

  26. lazy val recall: Double

    Returns document-based recall averaged by the number of documents

  27. lazy val subsetAccuracy: Double

    Returns subset accuracy (for equal sets of labels)

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

    Definition Classes
    AnyRef

  29. def toString(): String

    Definition Classes
    AnyRef → Any

  30. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped