hist_binning_CV {CalibratR}

hist_binning_CV

Description

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.

Usage

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

Value

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