Object

evaluation

Evaluation

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object Evaluation

Provides functions for evaluation of algorithm performance

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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. def MAE(y_pred: List[Double], y_true: List[Double]): Double

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    Calculates the Mean Absolute Error

    Calculates the Mean Absolute Error

    y_pred

    List of predictions

    y_true

    List of labels

  5. def MSE(y_pred: List[Double], y_true: List[Double]): Double

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    Calculates the Mean Squared Error

    Calculates the Mean Squared Error

    y_pred

    List of predictions

    y_true

    List of labels

  6. def MSLE(y_pred: List[Double], y_true: List[Double]): Double

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    Calculates the Mean Squared Logarithmic Error

    Calculates the Mean Squared Logarithmic Error

    y_pred

    List of predictions

    y_true

    List of labels

  7. def MedAE(y_pred: List[Double], y_true: List[Double]): Double

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    Calculates the Median Absolute Error

    Calculates the Median Absolute Error

    y_pred

    List of predictions

    y_true

    List of labels

  8. def RSqared(y_pred: List[Double], y_true: List[Double]): Double

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    Calculates the R^2 score

    Calculates the R^2 score

    y_pred

    List of predictions

    y_true

    List of labels

  9. final def asInstanceOf[T0]: T0

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  10. def clone(): AnyRef

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

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  12. def equals(arg0: Any): Boolean

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  13. def explainedVariance(y_pred: List[Double], y_true: List[Double]): Double

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    Calculates the Explained Variance Score

    Calculates the Explained Variance Score

    y_pred

    List of predictions

    y_true

    List of labels

  14. def f1(y_pred: List[Int], y_true: List[Int]): Double

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    Calculates the F1 score

    Calculates the F1 score

    y_pred

    List of predictions

    y_true

    List of labels

  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. def logLoss(p_pred: List[Double], y_true: List[Int], eps: Double = 1e-15): Double

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    Calculates the log loss

    Calculates the log loss

    p_pred

    List of probabilities for each instance

    y_true

    List of labels

    eps

    Clip probabilities to avoid undefined logs at p = 0, 1

  20. def matrix(y_pred: List[Int], y_true: List[Int], percentage: Boolean = false): Unit

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    Prints the confusion matrix

    Prints the confusion matrix

    y_pred

    List of predictions

    y_true

    List of labels

    percentage

    Display as percentage values

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

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  22. final def notify(): Unit

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  23. final def notifyAll(): Unit

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  24. def precision(y_pred: List[Int], y_true: List[Int]): Double

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    Calculates the precision score

    Calculates the precision score

    y_pred

    List of predictions

    y_true

    List of labels

  25. def recall(y_pred: List[Int], y_true: List[Int]): Double

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    Calculates the recall score

    Calculates the recall score

    y_pred

    List of predictions

    y_true

    List of labels

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

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  27. def toString(): String

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  28. final def wait(): Unit

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

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

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