Postprocessing of Rule Classification Models Learnt on Quantized
Data
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Documentation for package ‘qCBA’ version 1.0
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 | qCBARuleModel |
| qCBARuleModel-class | qCBARuleModel |
| 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 |