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

Description Usage Arguments Details Value Author(s) Examples

View source: R/predict_calibratR_parallel.R

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

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

`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

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

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

Johanna Schwarz

 ## 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)