hist_binning_CV function

hist_binning_CV

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)

Arguments

• actual: vector of observed class labels (0/1)
• predicted: vector of uncalibrated predictions
• n_bins: number of bins used in the histogram binning scheme, Default: 15
• n_folds: number of folds in the cross-validation, Default: 10
• seed: random seed to alternate the split of data set partitions
• input: specify if the input was scaled or transformed, scaled=1, transformed=2

Returns

list object containing the following components: - error: list object that summarizes discrimination and calibration errors obtained during the CV

• type: "hist"

• probs_CV: vector of calibrated predictions that was used during the CV

• actual_CV: respective vector of true values (0 or 1) that was used during the CV