For regression models, a list with the following components is returned:
y_hatPredictions for the observations computed on the fold for which the observation was omitted from the training set.
L1_errAggregate L1 error across the folds.
L2_errAggregate L1 error across the folds.
rmseAggregate RMSE across the folds.
foldsVector of indices specifying which fold each observation belonged to.
For classification models, a list with the following components is returned:
y_hatClass predictions for the observations computed on the fold for which the observation was omitted from the training set.
p_hatProbability estimates for the observations computed on the fold for which the observation was omitted from the training set.
confusion_matrixAggregate confusion matrix across the folds.
misclassification_errorTotal misclassification error across the folds.
foldsVector of indices specifying which fold each observation belonged to.