Class

org.apache.spark.mllib.evaluation

MultilabelMetrics

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class MultilabelMetrics extends AnyRef

Evaluator for multilabel classification.

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@Since( "1.2.0" )
Source
MultilabelMetrics.scala
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Instance Constructors

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

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    predictionAndLabels

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

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    @Since( "1.2.0" )

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. lazy val accuracy: Double

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    Returns accuracy

    Returns accuracy

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    @Since( "1.2.0" )
  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def f1Measure(label: Double): Double

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    Returns f1-measure for a given label (category)

    Returns f1-measure for a given label (category)

    label

    the label.

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    @Since( "1.2.0" )
  10. lazy val f1Measure: Double

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

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

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    @Since( "1.2.0" )
  11. def finalize(): Unit

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

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  13. lazy val hammingLoss: Double

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    Returns Hamming-loss

    Returns Hamming-loss

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    @Since( "1.2.0" )
  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. lazy val labels: Array[Double]

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    Returns the sequence of labels in ascending order

    Returns the sequence of labels in ascending order

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    @Since( "1.2.0" )
  17. lazy val microF1Measure: Double

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

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

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    @Since( "1.2.0" )
  18. lazy val microPrecision: Double

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

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

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    @Since( "1.2.0" )
  19. lazy val microRecall: Double

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

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

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    @Since( "1.2.0" )
  20. final def ne(arg0: AnyRef): Boolean

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  21. final def notify(): Unit

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  22. final def notifyAll(): Unit

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  23. def precision(label: Double): Double

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    Returns precision for a given label (category)

    Returns precision for a given label (category)

    label

    the label.

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    @Since( "1.2.0" )
  24. lazy val precision: Double

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    Returns document-based precision averaged by the number of documents

    Returns document-based precision averaged by the number of documents

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    @Since( "1.2.0" )
  25. def recall(label: Double): Double

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    Returns recall for a given label (category)

    Returns recall for a given label (category)

    label

    the label.

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    @Since( "1.2.0" )
  26. lazy val recall: Double

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    Returns document-based recall averaged by the number of documents

    Returns document-based recall averaged by the number of documents

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    @Since( "1.2.0" )
  27. lazy val subsetAccuracy: Double

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    Returns subset accuracy (for equal sets of labels)

    Returns subset accuracy (for equal sets of labels)

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    @Since( "1.2.0" )
  28. final def synchronized[T0](arg0: ⇒ T0): T0

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  29. def toString(): String

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  30. final def wait(): Unit

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

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

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