trait MultiTaskModelPredictionResult extends PredictionResult[Seq[Any]]
Container for predictions made on multiple labels simultaneously.
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- abstract 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
- PredictionResult
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- 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
- 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
- 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
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