case class BaggerHelper(models: ParSeq[Model[PredictionResult[Any]]], trainingData: Seq[(Vector[Any], Any)], Nib: Vector[Vector[Int]], useJackknife: Boolean, uncertaintyCalibration: Boolean) extends Product with Serializable
Helper class to subsume shared functionality of Bagger and MultiTaskBagger.
- models
collection of trained models
- trainingData
on which models were trained
- Nib
vector (over models) of vectors (over training data) of the number of repeats in each model's bag
- useJackknife
whether to use jackknife for uncertainty quantification
- uncertaintyCalibration
whether to apply empirical uncertainty calibration
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- new BaggerHelper(models: ParSeq[Model[PredictionResult[Any]]], trainingData: Seq[(Vector[Any], Any)], Nib: Vector[Vector[Int]], useJackknife: Boolean, uncertaintyCalibration: Boolean)
- models
collection of trained models
- trainingData
on which models were trained
- Nib
vector (over models) of vectors (over training data) of the number of repeats in each model's bag
- useJackknife
whether to use jackknife for uncertainty quantification
- uncertaintyCalibration
whether to apply empirical uncertainty calibration
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- val Nib: Vector[Vector[Int]]
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- lazy val biasTraining: Seq[(Vector[Any], Double)]
Data on which to train a bias learner.
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- val isRegression: Boolean
- val models: ParSeq[Model[PredictionResult[Any]]]
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- lazy val oobErrors: Seq[(Vector[Any], Double, Double)]
Seq of tuples containing training data paired with their out-of-bag residuals and uncertainty estimates.
Seq of tuples containing training data paired with their out-of-bag residuals and uncertainty estimates. Defined as lazy so it's only computed if needed for the ratio or bias learner calculations.
- def productElementNames: Iterator[String]
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- Product
- val ratio: Double
Calculate the uncertainty calibration ratio, which is the 68th percentile of error/uncertainty.
Calculate the uncertainty calibration ratio, which is the 68th percentile of error/uncertainty. for the training points. If a point has 0 uncertainty, the ratio is 1 iff error is also 0, or infinity otherwise. If the 68th percentile ratio is infinity, default to 1.0. This is not unreasonable when the number of training data and bags are small, meaning there may only be 2 out-of-bag models.
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- val trainingData: Seq[(Vector[Any], Any)]
- val uncertaintyCalibration: Boolean
- val useJackknife: Boolean
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