trait BaggedResult[+T] extends PredictionResult[T]

Interface defining the return value of a BaggedModel

This allows the implementation to depend on the number of simultaneous predictions, which has performance implications. For background on the uncertainty calculation, see Wager, S.; Hastie, T and Efron, B. Confidence Intervals for Random Forests: The Jackknife and Infinitesimal Jackknife. Journal of Machine Learning Research 15 (2014).

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PredictionResult[T], AnyRef, Any
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Abstract Value Members

  1. abstract def getExpected(): Seq[T]

    Get the expected values for this prediction

    Get the expected values for this prediction

    returns

    expected value of each prediction

    Definition Classes
    PredictionResult
  2. abstract def numPredictions: Int

    The number of inputs that have been predicted on (NOT the number of bagged models).

  3. abstract def predictions: Seq[PredictionResult[T]]

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
    Definition Classes
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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 clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: AnyRef): Boolean
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  8. def finalize(): Unit
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  9. final def getClass(): Class[_ <: AnyRef]
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    @native()
  10. def getGradient(): Option[Seq[Vector[Double]]]

    Average the gradients from the models in the ensemble

    Average the gradients from the models in the ensemble

    returns

    the gradient of each prediction as a vector of doubles

    Definition Classes
    BaggedResultPredictionResult
  11. def getImportanceScores(): Option[Seq[Seq[Double]]]

    Get the training row scores for each prediction

    Get the training row scores for each prediction

    returns

    sequence (over predictions) of sequence (over training rows) of importances

    Definition Classes
    PredictionResult
  12. def getInfluenceScores(actuals: Seq[Any]): Option[Seq[Seq[Double]]]

    Get the improvement (positive) or damage (negative) due to each training row on a prediction

    Get the improvement (positive) or damage (negative) due to each training row on a prediction

    actuals

    to assess the improvement or damage against

    returns

    Sequence (over predictions) of sequence (over training rows) of influence

    Definition Classes
    PredictionResult
  13. def getUncertainty(observational: Boolean = true): Option[Seq[Any]]

    Get the "uncertainty" of the prediction

    Get the "uncertainty" of the prediction

    For regression, this should be the TotalError if non-observational and the StdDevObs if observational

    observational

    whether the uncertainty should account for observational uncertainty

    returns

    uncertainty of each prediction

    Definition Classes
    PredictionResult
  14. def hashCode(): Int
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  15. final def isInstanceOf[T0]: Boolean
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  16. final def ne(arg0: AnyRef): Boolean
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  19. final def synchronized[T0](arg0: => T0): T0
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  20. def toString(): String
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  21. final def wait(): Unit
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  22. final def wait(arg0: Long, arg1: Int): Unit
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  23. final def wait(arg0: Long): Unit
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Inherited from PredictionResult[T]

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