object Crossvalidation
Implements utility functions for evaluating the quality of a registered dataset
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- type EvaluationFunction[D, DDomain[D] <: DiscreteDomain[D], A] = (PointDistributionModel[D, DDomain], DDomain[D]) => A
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- def leaveOneOutCrossvalidation[D, DDomain[D] <: DiscreteDomain[D], A](dataCollection: DataCollection[D, DDomain, EuclideanVector[D]], evalFun: EvaluationFunction[D, DDomain, A], biasModelAndRank: Option[LowRankGaussianProcess[D, EuclideanVector[D]]] = None)(implicit ndSpace: NDSpace[D], domainWarp: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]], rng: Random): Seq[Seq[A]]
Perform a leave one out crossvalidation.
Perform a leave one out crossvalidation. See nFoldCrossvalidation for details
- def nFoldCrossvalidation[D, DDomain[D] <: DiscreteDomain[D], A](numFolds: Int, dc: DataCollection[D, DDomain, EuclideanVector[D]], evalFun: EvaluationFunction[D, DDomain, A], biasModelAndRank: Option[LowRankGaussianProcess[D, EuclideanVector[D]]] = None)(implicit ndSpace: NDSpace[D], domainWarp: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]], rng: Random): Seq[Seq[A]]
Perform an n-fold crossvalidation.
Perform an n-fold crossvalidation. Given the chosen number of folds, this method will repeatedly split the data collection into a training and and a test set. A StatisticalMeshModel is then built from the training set of each fold. In case a biasModel is provided, this model is always added to the model built from the training data.
For each testing dataset in a fold, the evalFun is called to evaluate the quality of the model built from the training set.
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a sequence the size of the chosen number of folds that contains the sequence of evaluations for each data item in the fold's testing set, or an error if the model building for a fold failed.
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