R: Postprocessing of Rule Classification Models Learnt on Quantized Data

# Postprocessing of Rule Classification Models Learnt on Quantized Data

## Documentation for package ‘qCBA’ version 1.0.2

- DESCRIPTION file
- Package NEWS

## Help Pages

arulesCBA2arcCBAModel | arulesCBA2arcCBAModel Converts a model created by 'arulesCBA' so that it can be passed to qCBA
benchmarkQCBA | Learn and evaluate QCBA postprocessing on multiple rule learners. This can be, for example, used to automatically select the best model for a given use case based on a combined preference for accuracy and model size.
customCBARuleModel | customCBARuleModel
customCBARuleModel-class | customCBARuleModel
getConfVectorForROC | Returns vector with confidences for the positive class (useful for ROC or AUC computation)
mapDataTypes | Map R types to qCBA
predict.qCBARuleModel | Aplies qCBARuleModel
qcba | qCBA Quantitative CBA
qcbaHumTemp | Use the HumTemp dataset to test the one rule classification QCBA workflow.
qcbaIris | Use the iris dataset to the test QCBA workflow.
qcbaIris2 | Use the Iris dataset to test the experimental multi-rule QCBA workflow.
qCBARuleModel | QCBA Rule Model
qCBARuleModel-class | QCBA Rule Model
rcbaModel2CBARuleModel | rcbaModel2arcCBARuleModel Converts a model created by 'rCBA' so that it can be passed to qCBA
sbrlModel2arcCBARuleModel | sbrlModel2arcCBARuleModel Converts a model created by 'sbrl' so that it can be passed to qCBA
show-method | QCBA Rule Model