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com.github.rzykov.fastml4j.metric

BinaryClassificationMetrics

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class BinaryClassificationMetrics extends AnyRef

Calculates binary classification metrics based on a comparison of real and predicted labels

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Instance Constructors

  1. new BinaryClassificationMetrics(realLabels: INDArray, predictedLabels: INDArray)

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    realLabels

    NDArray of real labels

    predictedLabels

    NDArray of predicted lables

Type Members

  1. case class Confusion(predictedClass: Float, realClass: Float, qty: Int) extends Product with Serializable

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    class contains the result of confusion matrix

    class contains the result of confusion matrix

    predictedClass

    predicted class value

    realClass

    real class value

    qty

    number of cases

Value Members

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

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  2. final def ##(): Int

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  4. final def asInstanceOf[T0]: T0

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  5. def aucRoc: Float

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    calculates AUC value (area under ROC curve)

    calculates AUC value (area under ROC curve)

    returns

    AUC

  6. lazy val binTreshholds: Seq[Float]

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    stores unique sorted values of predicted labels

  7. def clone(): AnyRef

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  8. lazy val confusionMatrixByThreshold: Seq[(Float, Seq[Confusion])]

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    Calculates confusion matrix for different levels of threshold

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

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  11. def finalize(): Unit

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def precisionByThreshold: Seq[(Float, Float)]

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    Calculates Precision metrics for different levels of threshold

    Calculates Precision metrics for different levels of threshold

    returns

    collection of floats with pair: (threshold, precision)

  19. val predictedLabels: INDArray

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    NDArray of predicted lables

  20. val realLabels: INDArray

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    NDArray of real labels

  21. def recallByTreshold: Seq[(Float, Float)]

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    Calculates Recall metrics for different levels of threshold

    Calculates Recall metrics for different levels of threshold

    returns

    collection of floats with pair: (threshold, recall)

  22. def roc: Seq[(Float, Float)]

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    Computes the ROC curve

    Computes the ROC curve

    returns

    collection of pairs (fpr, tpr)

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

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  24. def toString(): String

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  25. final def wait(): Unit

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

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

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