uncalibrated_CV {CalibratR}

uncalibrated_CV

Description

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.

Usage

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

Value

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