UBL: An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks

Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.

Version: 0.0.9
Depends: R (≥ 3.0), methods, grDevices, graphics, stats, MBA, gstat, automap, sp, randomForest
Suggests: MASS, rpart, testthat, DMwR2, ggplot2, e1071
Published: 2023-10-07
DOI: 10.32614/CRAN.package.UBL
Author: Paula Branco [aut, cre], Rita Ribeiro [aut, ctb], Luis Torgo [aut, ctb]
Maintainer: Paula Branco <paobranco at gmail.com>
BugReports: https://github.com/paobranco/UBL/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/paobranco/UBL
NeedsCompilation: yes
Citation: UBL citation info
CRAN checks: UBL results


Reference manual: UBL.pdf


Package source: UBL_0.0.9.tar.gz
Windows binaries: r-devel: UBL_0.0.9.zip, r-release: UBL_0.0.9.zip, r-oldrel: UBL_0.0.9.zip
macOS binaries: r-release (arm64): UBL_0.0.9.tgz, r-oldrel (arm64): UBL_0.0.9.tgz, r-release (x86_64): UBL_0.0.9.tgz, r-oldrel (x86_64): UBL_0.0.9.tgz
Old sources: UBL archive

Reverse dependencies:

Reverse imports: MSclassifR


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