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, <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.

Version:	0.1.2.10
Suggests:	parallel
Published:	2019-07-28
Author:	David Cortes
Maintainer:	David Cortes <david.cortes.rivera at gmail.com>
License:	BSD_2_clause + file LICENSE
URL:	https://github.com/david-cortes/costsensitive
NeedsCompilation:	yes
CRAN checks:	costsensitive results

Documentation:

Reference manual:

costsensitive.pdf

Downloads:

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

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