org.apache.spark.mllib.evaluation

MulticlassMetrics

class MulticlassMetrics extends AnyRef

::Experimental:: Evaluator for multiclass classification.

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@Experimental()
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Instance Constructors

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

    predictionAndLabels

    an RDD of (prediction, label) pairs.

Value Members

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

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

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  3. final def ##(): Int

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

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  7. def clone(): AnyRef

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    @throws( ... )
  8. def confusionMatrix: Matrix

    Returns confusion matrix: predicted classes are in columns, they are ordered by class label ascending, as in "labels"

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

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  10. def equals(arg0: Any): Boolean

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  11. def fMeasure(label: Double): Double

    Returns f1-measure for a given label (category)

    Returns f1-measure for a given label (category)

    label

    the label.

  12. def fMeasure(label: Double, beta: Double): Double

    Returns f-measure for a given label (category)

    Returns f-measure for a given label (category)

    label

    the label.

    beta

    the beta parameter.

  13. lazy val fMeasure: Double

    Returns f-measure (equals to precision and recall because precision equals recall)

  14. def falsePositiveRate(label: Double): Double

    Returns false positive rate for a given label (category)

    Returns false positive rate for a given label (category)

    label

    the label.

  15. def finalize(): Unit

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

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  17. def hashCode(): Int

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  18. final def isInstanceOf[T0]: Boolean

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  19. lazy val labels: Array[Double]

    Returns the sequence of labels in ascending order

  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

    Returns precision for a given label (category)

    Returns precision for a given label (category)

    label

    the label.

  24. lazy val precision: Double

    Returns precision

  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 recall (equals to precision for multiclass classifier because sum of all false positives is equal to sum of all false negatives)

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

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  28. def toString(): String

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  29. def truePositiveRate(label: Double): Double

    Returns true positive rate for a given label (category)

    Returns true positive rate for a given label (category)

    label

    the label.

  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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  33. def weightedFMeasure(beta: Double): Double

    Returns weighted averaged f-measure

    Returns weighted averaged f-measure

    beta

    the beta parameter.

  34. lazy val weightedFMeasure: Double

    Returns weighted averaged f1-measure

  35. lazy val weightedFalsePositiveRate: Double

    Returns weighted false positive rate

  36. lazy val weightedPrecision: Double

    Returns weighted averaged precision

  37. lazy val weightedRecall: Double

    Returns weighted averaged recall (equals to precision, recall and f-measure)

  38. lazy val weightedTruePositiveRate: Double

    Returns weighted true positive rate (equals to precision, recall and f-measure)

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