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io.citrine.lolo.validation

CrossValidation

object CrossValidation

Methods tha use cross-validation to calculate predicted-vs-actual data and metric estimates

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  12. def kFoldCrossvalidation[T](trainingData: Seq[(Vector[Any], T)], learner: Learner, metrics: Map[String, Merit[T]], k: Int = 8, nTrial: Int = 1): Map[String, (Double, Double)]

    Driver to apply named metrics to k-fold cross-validated predicted-vs-actual

    Driver to apply named metrics to k-fold cross-validated predicted-vs-actual

    T

    type of the response, e.g. Double for scalar regression

    trainingData

    to cross-validate with

    learner

    to cross-validate

    metrics

    apply to the predicted-vs-actual data

    k

    number of folds

    nTrial

    number of times to refold the data to improve statistics

    returns

    a Map from the name of the metric to its mean value and the error in that mean

  13. def kFoldPvA[T](trainingData: Seq[(Vector[Any], T)], learner: Learner, k: Int = 8, nTrial: Int = 1): Iterable[(PredictionResult[T], Seq[T])]

    Use k-fold cross-validation to create predicted vs actual results

    Use k-fold cross-validation to create predicted vs actual results

    T

    type of the response, e.g. Double for scalar regression

    trainingData

    to cross-validate with

    learner

    to cross-validate

    k

    number of folds

    nTrial

    number of times to re-fold the data to improve statistics

    returns

    an iterable over predicted-vs-actual for each fold

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