uncalibrated_CV function

uncalibrated_CV

performs n_folds-CV but with only input-preprocessing the test set. No calibration model is trained and evaluated in this method. The predicted values are partitioned into n subsets. The training set is constructed on (n-1) subsets; the remaining set is used for testing. Since no calibration model is used in this method, the test set predictions are only input-preprocessed (either scaled or transformed, depending on input). All test set predictions are merged and used to compute error metrics for the input-preprocessing methods.

uncalibrated_CV(actual, predicted, n_folds = 10, seed, input)

Arguments

• actual: vector of observed class labels (0/1)
• predicted: vector of uncalibrated predictions
• n_folds: number of folds for 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: "uncalibrated"

• probs_CV: vector of input-preprocessed predictions that was used during the CV

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