Resum
In the field of supervised machine learning, the precise evaluation of classification models stands as a fundamental pursuit. This necessitates the utilization of robust performance metrics. This repository focuses specifically on the evaluation of classification models within the contexts of ordinal and interval scale classifications. It contains the implementation designed to compute the results expounded upon in the article "Adapting Ordinal Performance Metrics for Interval Scaling: Length Matters" by G. Binotto and R. Delgado (preprint, 2024). Precisely, it facilitates the computation of two ordinal metrics, namely Mean Absolute Error (MAE) and Total Cost (TC), alongside their interval scale counterparts. A specific section is designed to address scenarios in which the rightmost interval is unbounded.
Idioma original | Anglès |
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Format de la publicació | Internet |
Estat de la publicació | Publicada - 2024 |