This package is considered a duplicate. The official version of this package is found at:https://kliegr.r-universe.dev/qCBA

Package: qCBA 1.0.2

Tomáš Kliegr

qCBA: Postprocessing of Rule Classification Models Learnt on Quantized Data

Implements the Quantitative Classification-based on Association Rules (QCBA) algorithm (<doi:10.1007/s10489-022-04370-x>). 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.

Authors:Tomáš Kliegr [aut, cre]

qCBA_1.0.2.tar.gz
qCBA_1.0.2.zip(r-4.7-any)qCBA_1.0.2.zip(r-4.6-any)
qCBA_1.0.2.tar.gz(r-4.7-any)qCBA_1.0.2.tar.gz(r-4.6-any)
qCBA_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
qCBA/json (API)

# Install 'qCBA' in R:

install.packages('qCBA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kliegr/qcba/issues

Uses libs:

openjdk– OpenJDK Java runtime, using Hotspot JIT

On CRAN:

openjdk

1.00 score 254 downloads 11 exports 19 dependencies

Last updated from:0d00601a24. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel	OK	122
source / vignettes	OK	197
linux-release	OK	177
windows-devel	OK	119
windows-release	OK	114
wasm-release	OK	112

Exports:arulesCBA2arcCBAModel benchmarkQCBA getConfVectorForROC mapDataTypes qcba qcbaHumTemp qcbaIris qcbaIris2 rcbaModel2CBARuleModel sbrlModel2arcCBARuleModel show

Dependencies:arc arules arulesCBA codetools discretization foreach generics glmnet iterators lattice Matrix R.methodsS3 R.oo R.utils Rcpp RcppEigen rJava shape survival

Help page	Topics
arulesCBA2arcCBAModel Converts a model created by 'arulesCBA' so that it can be passed to qCBA	arulesCBA2arcCBAModel
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.	benchmarkQCBA
customCBARuleModel	customCBARuleModel customCBARuleModel-class
Returns vector with confidences for the positive class (useful for ROC or AUC computation)	getConfVectorForROC
Map R types to qCBA	mapDataTypes
Aplies qCBARuleModel	predict.qCBARuleModel
qCBA Quantitative CBA	qcba
Use the HumTemp dataset to test the one rule classification QCBA workflow.	qcbaHumTemp
Use the iris dataset to the test QCBA workflow.	qcbaIris
Use the Iris dataset to test the experimental multi-rule QCBA workflow.	qcbaIris2
QCBA Rule Model	qCBARuleModel qCBARuleModel-class show,qCBARuleModel-method
rcbaModel2arcCBARuleModel Converts a model created by 'rCBA' so that it can be passed to qCBA	rcbaModel2CBARuleModel
sbrlModel2arcCBARuleModel Converts a model created by 'sbrl' so that it can be passed to qCBA	sbrlModel2arcCBARuleModel

Package: qCBA 1.0.2

qCBA: Postprocessing of Rule Classification Models Learnt on Quantized Data

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)