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qCBA  

Postprocessing of Rule Classification Models Learnt on Quantized Data
View on CRAN: Click here


Download and install qCBA package within the R console
Install from CRAN:
install.packages("qCBA")

Install from Github:
library("remotes")
install_github("cran/qCBA")

Install by package version:
library("remotes")
install_version("qCBA", "1.0")



Attach the package and use:
library("qCBA")
Maintained by
Tomáš Kliegr
[Scholar Profile | Author Map]
First Published: 2017-12-01
Latest Update: 2020-11-19
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 .
How to cite:
Tomáš Kliegr (2017). qCBA: Postprocessing of Rule Classification Models Learnt on Quantized Data. R package version 1.0, https://cran.r-project.org/web/packages/qCBA. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.3.1 (2018-01-12 18:20), 0.3 (2017-12-01 19:11), 0.4 (2020-01-15 17:10), 0.5.1 (2020-11-19 09:30)
Other packages that cited qCBA R package
View qCBA citation profile
Other R packages that qCBA depends, imports, suggests or enhances
Complete documentation for qCBA
Functions, R codes and Examples using the qCBA R package
Some associated functions: arulesCBA2arcCBAModel . customCBARuleModel-class . getConfVectorForROC . mapDataTypes . predict.qCBARuleModel . qCBARuleModel-class . qcba . qcbaHumTemp . qcbaIris . qcbaIris2 . rcbaModel2CBARuleModel . sbrlModel2arcCBARuleModel . 
Some associated R codes: onLoad.R . rMARC.R .  Full qCBA package functions and examples
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