case class MultiTaskBaggedResult(baggedPredictions: Seq[BaggedResult[Any]], realLabels: Seq[Boolean], NibIn: Vector[Vector[Int]]) extends BaggedResult[Seq[Any]] with MultiTaskModelPredictionResult with Product with Serializable
Container with model-wise predictions for each label and the machinery to compute (co)variance.
- baggedPredictions
bagged prediction results for each label
- realLabels
a boolean sequence indicating which labels are real-valued
- NibIn
the sampling matrix as (# bags) x (# training)
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- MultiTaskBaggedResult
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- MultiTaskModelPredictionResult
- BaggedResult
- PredictionResult
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Instance Constructors
- new MultiTaskBaggedResult(baggedPredictions: Seq[BaggedResult[Any]], realLabels: Seq[Boolean], NibIn: Vector[Vector[Int]])
- baggedPredictions
bagged prediction results for each label
- realLabels
a boolean sequence indicating which labels are real-valued
- NibIn
the sampling matrix as (# bags) x (# training)
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- lazy val Nib: Vector[Vector[Int]]
- val NibIn: Vector[Vector[Int]]
- final def asInstanceOf[T0]: T0
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- val baggedPredictions: Seq[BaggedResult[Any]]
- def clone(): AnyRef
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- def getExpected(): Seq[Seq[Any]]
Get the expected values for this prediction
Get the expected values for this prediction
- returns
expected value of each prediction
- Definition Classes
- MultiTaskBaggedResult → PredictionResult
- 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
- BaggedResult → PredictionResult
- 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
- 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
- def getUncertainty(observational: Boolean = true): Option[Seq[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
- MultiTaskBaggedResult → MultiTaskModelPredictionResult → PredictionResult
- def getUncertaintyCorrelation(i: Int, j: Int, observational: Boolean = true): Option[Seq[Double]]
Get the correlation coefficients between the predictions made on two labels.
Get the correlation coefficients between the predictions made on two labels. Correlation coefficient is bounded between -1 and 1. If either index is out of bounds or does not correspond to a real-valued label, then this method must return None.
- i
index of the first label
- j
index of the second label
- observational
whether the uncertainty correlation should take observational noise into account
- returns
optional sequence of correlation coefficients between specified labels for each prediction
- Definition Classes
- MultiTaskBaggedResult → MultiTaskModelPredictionResult
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- def numPredictions: Int
The number of inputs that have been predicted on (NOT the number of bagged models).
The number of inputs that have been predicted on (NOT the number of bagged models).
- Definition Classes
- MultiTaskBaggedResult → BaggedResult
- def predictions: Seq[PredictionResult[Seq[Any]]]
- Definition Classes
- MultiTaskBaggedResult → BaggedResult
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- val realLabels: Seq[Boolean]
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