costsensitive: Cost-Sensitive Multi-Class Classification

Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation. Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <>) and Regression One-Vs-Rest. Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers that don't accept observation weights.

Suggests: parallel
Published: 2019-07-28
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera at>
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
CRAN checks: costsensitive results


Reference manual: costsensitive.pdf


Package source: costsensitive_0.1.2.10.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): costsensitive_0.1.2.10.tgz, r-oldrel (arm64): costsensitive_0.1.2.10.tgz, r-release (x86_64): costsensitive_0.1.2.10.tgz
Old sources: costsensitive archive


Please use the canonical form to link to this page.