Package: costsensitive 0.1.2.10

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, Langford, & Zadrozny (2008) <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer,Dani, Hayes, Langford, Zadrozny, (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.

Authors:David Cortes