keystoneml.evaluation

BinaryClassificationMetrics

case class BinaryClassificationMetrics(tp: Double, fp: Double, tn: Double, fn: Double) extends Product with Serializable

Contains the contingency table for a binary classifier, and provides common metrics such as precision & recall

Similar to MLlib's org.apache.spark.mllib.evaluation.BinaryClassificationMetrics, but only for metrics that can be calculated from the contingency table

tp

True positive count

fp

False positive count

tn

True negative count

fn

False negative count

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Instance Constructors

  1. new BinaryClassificationMetrics(tp: Double, fp: Double, tn: Double, fn: Double)

    tp

    True positive count

    fp

    False positive count

    tn

    True negative count

    fn

    False negative count

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. def accuracy: Double

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  10. def error: Double

  11. def fScore(beta: Double = 1.0): Double

    Calculate the f_beta-score for the binary classifier

    Calculate the f_beta-score for the binary classifier

    "Measures the effectiveness of retrieval with respect to a user who attaches beta times as much importance to recall as precision" http://en.wikipedia.org/wiki/F1_score

    beta

    Defaults to 1 (so returns the f1-score)

  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  13. val fn: Double

    False negative count

  14. val fp: Double

    False positive count

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

    Definition Classes
    AnyRef → Any

  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  17. def merge(other: BinaryClassificationMetrics): BinaryClassificationMetrics

    Merge this contingency table with another

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

    Definition Classes
    AnyRef

  19. final def notify(): Unit

    Definition Classes
    AnyRef

  20. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  21. def precision: Double

  22. def recall: Double

  23. def specificity: Double

  24. def summary(format: String = "%2.3f"): 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)

    returns

    the pretty-printed string

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

    Definition Classes
    AnyRef

  26. val tn: Double

    True negative count

  27. val tp: Double

    True positive count

  28. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
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    @throws( ... )

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

    Definition Classes
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    @throws( ... )

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