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trains and evaluates the histogram binning calibration model repeated folds-Cross-Validation (CV).
The predicted values are partitioned into n subsets. A histogram binning model is constructed on (n-1) subsets; the remaining set is used
for testing the model. All test set predictions are merged and used to compute error metrics for the model.
hist_binning_CV(actual, predicted, n_bins = 15, n_folds = 10, seed, input)vector of observed class labels (0/1)
vector of uncalibrated predictions
number of bins used in the histogram binning scheme, Default: 15
number of folds in the cross-validation, Default: 10
random seed to alternate the split of data set partitions
specify if the input was scaled or transformed, scaled=1, transformed=2
list object containing the following components:
list object that summarizes discrimination and calibration errors obtained during the CV
"hist"
vector of calibrated predictions that was used during the CV
respective vector of true values (0 or 1) that was used during the CV