MulticlassMetrics

class MulticlassMetrics extends AnyRef

::Experimental:: Evaluator for multiclass classification.

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@Since( "1.1.0" ) ()
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Instance Constructors

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

predictionAndLabels

an RDD of (prediction, label) pairs.

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@Since( "1.1.0" )

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

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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protected[java.lang]
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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"

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

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@Since( "1.1.0" )
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.

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@Since( "1.1.0" )
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.

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@Since( "1.1.0" )
13. lazy val fMeasure: Double

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

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

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@Since( "1.1.0" )
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.

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@Since( "1.1.0" )
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

Returns the sequence of labels in ascending order

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@Since( "1.1.0" )
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

Returns precision for a given label (category)

Returns precision for a given label (category)

label

the label.

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@Since( "1.1.0" )
24. lazy val precision: Double

Returns precision

Returns precision

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@Since( "1.1.0" )
25. def recall(label: Double): Double

Returns recall for a given label (category)

Returns recall for a given label (category)

label

the label.

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@Since( "1.1.0" )
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)

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

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@Since( "1.1.0" )
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.

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@Since( "1.1.0" )
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.

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@Since( "1.1.0" )
34. lazy val weightedFMeasure: Double

Returns weighted averaged f1-measure

Returns weighted averaged f1-measure

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@Since( "1.1.0" )
35. lazy val weightedFalsePositiveRate: Double

Returns weighted false positive rate

Returns weighted false positive rate

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@Since( "1.1.0" )
36. lazy val weightedPrecision: Double

Returns weighted averaged precision

Returns weighted averaged precision

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@Since( "1.1.0" )
37. lazy val weightedRecall: Double

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

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

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@Since( "1.1.0" )
38. lazy val weightedTruePositiveRate: Double

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

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

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@Since( "1.1.0" )