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qCBA (version 1.0.2)

Postprocessing of Rule Classification Models Learnt on Quantized Data

Description

Implements the Quantitative Classification-based on Association Rules (QCBA) algorithm (). QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. Supported are 'CBA' implementations from 'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR', 'FOIL2' and 'PRM' implementations from 'arulesCBA' package and 'SBRL' implementation from the 'sbrl' package. The result of the post-processing is an ordered CBA-like rule list.

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Install

install.packages('qCBA')

Monthly Downloads

212

Version

1.0.2

License

GPL-3

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Maintainer

Tomas Kliegr

Last Published

April 3rd, 2025

Functions in qCBA (1.0.2)

qCBARuleModel-class

QCBA Rule Model
qcba

qCBA Quantitative CBA
qcbaHumTemp

Use the HumTemp dataset to test the one rule classification QCBA workflow.
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
customCBARuleModel-class

customCBARuleModel
qcbaIris

Use the iris dataset to the test QCBA workflow.
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.
rcbaModel2CBARuleModel

rcbaModel2arcCBARuleModel Converts a model created by rCBA so that it can be passed to qCBA
qcbaIris2

Use the Iris dataset to test the experimental multi-rule QCBA workflow.
sbrlModel2arcCBARuleModel

sbrlModel2arcCBARuleModel Converts a model created by sbrl so that it can be passed to qCBA