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costsensitive  

Cost-Sensitive Multi-Class Classification
View on CRAN: Click here


Download and install costsensitive package within the R console
Install from CRAN:
install.packages("costsensitive")

Install from Github:
library("remotes")
install_github("cran/costsensitive")

Install by package version:
library("remotes")
install_version("costsensitive", "0.1.2.10")



Attach the package and use:
library("costsensitive")
Maintained by
David Cortes
[Scholar Profile | Author Map]
First Published: 2019-03-03
Latest Update: 2019-07-28
Description:
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, ), 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.
How to cite:
David Cortes (2019). costsensitive: Cost-Sensitive Multi-Class Classification. R package version 0.1.2.10, https://cran.r-project.org/web/packages/costsensitive. Accessed 10 Apr. 2025.
Previous versions and publish date:
0.1.2.1 (2019-03-03 12:00)
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Complete documentation for costsensitive
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