case object CrossValidation extends Product with Serializable
Methods tha use cross-validation to calculate predicted-vs-actual data and metric estimates
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- def kFoldCrossvalidation[T](trainingData: Seq[(Vector[Any], T)], learner: Learner, metrics: Map[String, Merit[T]], k: Int = 8, nTrial: Int = 1, rng: Random = Random): 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
- rng
random number generator to use in choosing folds
- returns
a Map from the name of the metric to its mean value and the error in that mean
- def kFoldPvA[T](trainingData: Seq[(Vector[Any], T)], learner: Learner, k: Int = 8, nTrial: Int = 1, rng: Random = Random): 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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