Binary-Classification Performance-Metrics Benchmarking Data
收藏NIAID Data Ecosystem2026-03-12 收录
下载链接：
https://data.mendeley.com/datasets/2g36672s5f
下载链接
链接失效反馈官方服务：
资源简介：
This data provides the detailed test results of the benchmarking for the binary-classification performance metrics. The benchmark comprising three stages was applied on 13 metrics namely True Positive Rate, True Negative Rate, Positive Predictive Value, Negative Predictive Value, Accuracy, Informedness, Markedness, Balanced Accuracy, G, Normalized Mutual Information, F1, Cohen’s Kappa, and Mathew’s Correlation Coefficient (MCC).
The new benchmarking method is described in Gürol Canbek, Tugba Taskaya Temizel, and Seref Sagiroglu, "BenchMetrics: A Systematic Benchmarking Method for Binary-Classification Performance Metrics", Neural Computing and Applications, 2020 (Submitted).
The related benchmarking API and an interactive ready-to-run experimentation platform is available online (see the references)
创建时间：
2020-09-14
