org.apache.spark.mllib.evaluation

MultilabelMetrics

class MultilabelMetrics extends AnyRef

Evaluator for multilabel classification.

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@Since( "1.2.0" )
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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.

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    @Since( "1.2.0" )

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

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  3. final def ##(): Int

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  6. lazy val accuracy: Double

    Returns accuracy

    Returns accuracy

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    @Since( "1.2.0" )

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  8. def clone(): AnyRef

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

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  10. def equals(arg0: Any): Boolean

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  11. def f1Measure(label: Double): Double

    Returns f1-measure for a given label (category)

    Returns f1-measure for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.2.0" )

  12. lazy val f1Measure: Double

    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" )

  13. def finalize(): Unit

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

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  15. lazy val hammingLoss: Double

    Returns Hamming-loss

    Returns Hamming-loss

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    @Since( "1.2.0" )

  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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  18. lazy val labels: Array[Double]

    Returns the sequence of labels in ascending order

    Returns the sequence of labels in ascending order

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    @Since( "1.2.0" )

  19. lazy val microF1Measure: Double

    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)

    Annotations
    @Since( "1.2.0" )

  20. lazy val microPrecision: Double

    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" )

  21. lazy val microRecall: Double

    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" )

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  23. final def notify(): Unit

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  24. final def notifyAll(): Unit

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  25. def precision(label: Double): Double

    Returns precision for a given label (category)

    Returns precision for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.2.0" )

  26. lazy val precision: Double

    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" )

  27. def recall(label: Double): Double

    Returns recall for a given label (category)

    Returns recall for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.2.0" )

  28. lazy val recall: Double

    Returns document-based recall averaged by the number of documents

    Returns document-based recall averaged by the number of documents

    Annotations
    @Since( "1.2.0" )

  29. lazy val subsetAccuracy: Double

    Returns subset accuracy (for equal sets of labels)

    Returns subset accuracy (for equal sets of labels)

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    @Since( "1.2.0" )

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

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  31. def toString(): String

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