Packages

c

io.citrine.lolo.bags

BaggedResult

class BaggedResult extends PredictionResult[Any]

Container with model-wise predictions and logic to compute variances and training row scores

Linear Supertypes
PredictionResult[Any], AnyRef, Any
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  1. BaggedResult
  2. PredictionResult
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Visibility
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Instance Constructors

  1. new BaggedResult(predictions: Seq[PredictionResult[Any]], NibIn: Vector[Vector[Int]], useJackknife: Boolean, bias: Option[Seq[Double]] = None, repInput: Vector[Any])

    predictions

    for each constituent model

    NibIn

    the sample matrix as (N_models x N_training)

    bias

    model to use for estimating bias

    repInput

    representative input

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. lazy val Nib: Vector[Vector[Int]]
  5. lazy val NibIJMat: DenseMatrix[Double]
  6. lazy val NibJMat: DenseMatrix[Double]
  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. lazy val expected: Seq[Any]
  12. lazy val expectedMatrix: Seq[Seq[Any]]
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  15. def getExpected(): Seq[Any]

    Return the ensemble average or maximum vote

    Return the ensemble average or maximum vote

    returns

    expected value of each prediction

    Definition Classes
    BaggedResult → PredictionResult
  16. def getGradient(): Option[Seq[Vector[Double]]]

    Get the gradient or sensitivity of each prediction

    Get the gradient or sensitivity of each prediction

    returns

    a vector of doubles for each prediction

    Definition Classes
    BaggedResult → PredictionResult
  17. def getImportanceScores(): Option[Seq[Seq[Double]]]

    Get the training row scores for each prediction

    Get the training row scores for each prediction

    returns

    training row scores of each prediction

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

    Return IJ scores

    Return IJ scores

    actuals

    to assess the improvement or damage against

    returns

    training row scores of each prediction

    Definition Classes
    BaggedResult → PredictionResult
  19. def getUncertainty(): Option[Seq[Any]]

    Return jackknife-based variance estimates

    Return jackknife-based variance estimates

    returns

    uncertainty of each prediction

    Definition Classes
    BaggedResult → PredictionResult
  20. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  21. def influences(meanPrediction: Vector[Double], actualPrediction: Vector[Double], modelPredictions: Seq[Seq[Double]], NibJ: DenseMatrix[Double], NibIJ: DenseMatrix[Double]): Seq[Vector[Double]]

    Compute the IJ training row scores for a prediction

    Compute the IJ training row scores for a prediction

    meanPrediction

    over the models

    modelPredictions

    prediction of each model

    NibJ

    sampling matrix for the jackknife-after-bootstrap estimate

    NibIJ

    sampling matrix for the infinitesimal jackknife estimate

    returns

    the score of each training row as a vector of doubles

  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  25. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  26. lazy val rep: Any
  27. def scores(meanPrediction: Vector[Double], modelPredictions: Seq[Seq[Double]], NibJ: DenseMatrix[Double], NibIJ: DenseMatrix[Double]): Seq[Vector[Double]]

    Compute the IJ training row scores for a prediction

    Compute the IJ training row scores for a prediction

    meanPrediction

    over the models

    modelPredictions

    prediction of each model

    NibJ

    sampling matrix for the jackknife-after-bootstrap estimate

    NibIJ

    sampling matrix for the infinitesimal jackknife estimate

    returns

    the score of each training row as a vector of doubles

  28. lazy val scores: Seq[Vector[Double]]
  29. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  30. def toString(): String
    Definition Classes
    AnyRef → Any
  31. lazy val uncertainty: Seq[Any]
  32. def variance(meanPrediction: Vector[Double], modelPredictions: Seq[Seq[Double]], NibJ: DenseMatrix[Double], NibIJ: DenseMatrix[Double]): Seq[Double]

    Compute the variance of a prediction as the average of bias corrected IJ and J variance estimates

    Compute the variance of a prediction as the average of bias corrected IJ and J variance estimates

    meanPrediction

    over the models

    modelPredictions

    prediction of each model

    NibJ

    sampling matrix for the jackknife-after-bootstrap estimate

    NibIJ

    sampling matrix for the infinitesimal jackknife estimate

    returns

    the estimated variance

  33. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  34. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from PredictionResult[Any]

Inherited from AnyRef

Inherited from Any

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