EvaluationStatistics

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

   

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2. final def ##(): Int

   

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

   

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4. final def asInstanceOf[T0]: T0

   

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5. def classificationAccuracySignificance[A](predicted: Seq[A], baseline: Seq[A], actual: Seq[A], N_samples: Int = 10000): Double

   

   Calculate accuracy significance for labels predicted by a system over labels predicted by a baseline, compared to actual labels, using the bootstrap.

   Calculate accuracy significance for labels predicted by a system over labels predicted by a baseline, compared to actual labels, using the bootstrap.

   A

   The label type

   predicted

   The labels predicted by the treatment system

   baseline

   The labels predicted by the baseline (control) system

   actual

   The actual labels

   N_samples

   Number of samples to use in bootstrap

   returns

   The p-value significance of the accuracy statistic

6. def classificationSignificance[A](stat: (EvaluationStatistics[A]) ⇒ Double)(predicted: Seq[A], baseline: Seq[A], actual: Seq[A], N_samples: Int = 10000): Double

   

   Calculate significance of the given evaluation statistic for labels predicted by a system over labels predicted by a baseline, compared to actual labels, using the bootstrap.

   Calculate significance of the given evaluation statistic for labels predicted by a system over labels predicted by a baseline, compared to actual labels, using the bootstrap.

   A

   The label type

   stat

   A function of EvaluationStatistics, such as accuracy or microF1

   predicted

   The labels predicted by the treatment system

   baseline

   The labels predicted by the baseline (control) system

   actual

   The actual labels

   N_samples

   Number of samples to use in bootstrap

   returns

   The p-value of the statistic

7. def clone(): AnyRef

   

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10. def finalize(): Unit

    

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

    

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12. def hashCode(): Int

    

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13. final def isInstanceOf[T0]: Boolean

    

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14. def macroAverage[A](tables: Map[A, Table])(accessor: (Table) ⇒ Double): Double

    

15. def makeTables[A](predicted: Seq[A], actual: Seq[A]): Map[A, Table]

    

16. def makeTables[A](outcome: A)(predicted: Seq[A], actual: Seq[A]): Table

    

17. def microAverage[A](tables: Map[A, Table])(accessor: (Table) ⇒ Double): Double

    

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

    

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19. final def notify(): Unit

    

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23. final def wait(): Unit

    

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

    

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

    

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26. def weightedAverage[A](tables: Map[A, Table])(accessor: (Table) ⇒ Double): Double