uncalibrated_CV {CalibratR} | R Documentation |
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 |