Cost-Sensitive Multi-Class Classification R package costsensitive version 0.1.2.10 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, <https://dl.acm.org/citation.cfm?id=1102358>) 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. Software Suggests: parallel David Cortes <david.cortes.rivera@gmail.com> Comprehensive R Archive Network (CRAN) David Cortes BSD_2_clause + file LICENSE (https://CRAN.R-project.org/package=costsensitive/LICENSE) 2019-07-28 application/tgz https://CRAN.R-project.org/package=costsensitive doi:10.32614/CRAN.package.costsensitive