Postprocessing of Rule Classification Models Learnt on Quantized Data

Documentation for package ‘qCBA’ version 1.0.2

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