predict_calibratR function

predict_calibratR

maps the uncalibrated predictions new into calibrated predictions using the passed over calibration models

predict_calibratR(calibration_models, new = NULL, nCores = 4)

Arguments

• calibration_models: list of trained calibration models that were constructed using the calibrate method. The list components calibration_models from the calibrate output can be used directly.
• new: vector of new uncalibrated instances. Default: 100 scores from the minimum to the maximum of the original ML scores
• nCores: nCores how many cores should be used during parallelisation. Default: 4

Returns

list object with the following components: - predictions: a list containing the calibrated predictions for each calibration model

• significance_test_set: a list containing the percentage of new instances for which prediction estimates are statistically significant

• pred_per_bin: a list containing the number of instances in each bin for the binning models

Details

if no new value is given, the function will evaluate a sequence of numbers ranging from the minimum to the maximum of the original values in the training set

Examples

## Loading dataset in environment
 data(example)
 test_set <- example$test_set
 calibration_model <- example$calibration_model

 ## Predict for test set
predictions <-  predict_calibratR(calibration_model$calibration_models, new=test_set, nCores = 2)

Author(s)

Johanna Schwarz