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qCBA (version 0.5.1)

Quantitative Classification by Association Rules

Description

CBA postprocessing algorithm that creates smaller models for datasets containing quantitative (numerical) attributes. Article describing QCBA is published in Tomas Kliegr (2017) . The package can also postprocess results of the SBRL package, which is no longer in CRAN, but can be obtained from .

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Install

install.packages('qCBA')

Monthly Downloads

211

Version

0.5.1

License

GPL-3

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Maintainer

Tomas Kliegr

Last Published

November 19th, 2020

Functions in qCBA (0.5.1)

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
qCBARuleModel-class

qCBARuleModel
mapDataTypes

Map R types to qCBA
predict.qCBARuleModel

Aplies qCBARuleModel
qcba

qCBA Quantitative CBA
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
getConfVectorForROC

Returns vector with confidences for the positive class (useful for ROC or AUC computation)
customCBARuleModel-class

rCBARuleModel
qcbaIris2

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

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