keystoneml.evaluation

MulticlassMetrics

case class MulticlassMetrics(confusionMatrix: DenseMatrix[Double]) extends Product with Serializable

Contains the confusion matrix for a multiclass classifier, and provides common metrics such as micro & macro precision & recall

Similar to MLlib's org.apache.spark.mllib.evaluation.MulticlassMetrics, but only does one pass over the data to calculate everything

Sample metrics compared at: Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45, p. 427-437 http://rali.iro.umontreal.ca/rali/sites/default/files/publis/SokolovaLapalme-JIPM09.pdf

confusionMatrix

rows are the true labels, cols are the predicted labels

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Instance Constructors

  1. new MulticlassMetrics(confusionMatrix: DenseMatrix[Double])

    confusionMatrix

    rows are the true labels, cols are the predicted labels

Value Members

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

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

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  3. final def ##(): Int

    Definition Classes
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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

    Definition Classes
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  7. def avgAccuracy: Double

  8. def avgError: Double

  9. val classMetrics: Array[BinaryClassificationMetrics]

  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )

  11. val confusionMatrix: DenseMatrix[Double]

    rows are the true labels, cols are the predicted labels

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

    Definition Classes
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  13. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

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

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  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  16. def macroFScore(beta: Double = 1.0): Double

  17. def macroPrecision: Double

  18. def macroRecall: Double

  19. def microFScore(beta: Double = 1.0): Double

  20. def microPrecision: Double

  21. def microRecall: Double

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

    Definition Classes
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  23. final def notify(): Unit

    Definition Classes
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  24. final def notifyAll(): Unit

    Definition Classes
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  25. def pprintConfusionMatrix(classes: Array[String]): String

    Pretty-prints the confusion matrix in a style similar to Mahout.

    Pretty-prints the confusion matrix in a style similar to Mahout. Predicted labels are in columns. True labels in rows

    classes

    An array containing the class names, where the indices are the class labels

    returns

    the pretty-printed string

  26. def summary(classes: Array[String]): String

    Pretty-prints a summary of how the multiclass classifier did (including the confusion matrix)

    Pretty-prints a summary of how the multiclass classifier did (including the confusion matrix)

    classes

    An array containing the class names, where the indices are the class labels

    returns

    the pretty-printed string

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

    Definition Classes
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  28. def totalAccuracy: Double

  29. def totalError: Double

  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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