Crossvalidation

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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6. final def eq(arg0: AnyRef): Boolean

   

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12. def leaveOneOutCrossvalidation[A](dataCollection: DataCollection, evalFun: EvaluationFunction[A], biasModelAndRank: Option[(GaussianProcess[_3D, _3D], Int)] = None): Seq[Try[Seq[A]]]

    

    Perform a leave one out crossvalidation.

    Perform a leave one out crossvalidation. See nFoldCrossvalidation for details

13. def nFoldCrossvalidation[A](numFolds: Int, dc: DataCollection, evalFun: EvaluationFunction[A], biasModelAndRank: Option[(GaussianProcess[_3D, _3D], Int)] = None): Seq[Try[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.

14. final def ne(arg0: AnyRef): Boolean

    

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15. final def notify(): Unit

    

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21. final def wait(arg0: Long): Unit

    

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