BBQ_CV: BBQ_CV
In CalibratR: Mapping ML Scores to Calibrated Predictions

Description Usage Arguments Value Examples

trains and evaluates the BBQ calibration model using folds-Cross-Validation (CV). The predicted values are partitioned into n subsets. A BBQ 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.

1 2	BBQ_CV(actual, predicted, method_for_prediction = 0, n_folds = 10, seed, input)

`actual`	vector of observed class labels (0/1)
`predicted`	vector of uncalibrated predictions
`method_for_prediction`	0=selection, 1=averaging, Default: 0
`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

list object containing the following components:

`error`	list object that summarizes discrimination and calibration errors obtained during the CV
`pred_idx`	which BBQ prediction method was used during CV, 0=selection, 1=averaging
`type`	"BBQ"
`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

 ## Loading dataset in environment
 data(example)
 actual <- example$actual
 predicted <- example$predicted
 BBQ_model <- CalibratR:::BBQ_CV(actual, predicted, method_for_prediction=0, n_folds=4, 123, 1)