Comparing Classifiers: A Case Study Using PyCM

• URL: http://arxiv.org/abs/2602.13482v1
• Date: Fri, 13 Feb 2026 21:37:40 GMT
• Title: Comparing Classifiers: A Case Study Using PyCM
• Authors: Sadra Sabouri, Alireza Zolanvari, Sepand Haghighi,
• Abstract summary: We show how the choice of evaluation metrics can shift the interpretation of a model's efficacy.<n>Our findings emphasize that a multi-dimensional evaluation framework is essential for uncovering small but important differences in model performance.
• Score: 1.0052405518945386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Selecting an optimal classification model requires a robust and comprehensive understanding of the performance of the model. This paper provides a tutorial on the PyCM library, demonstrating its utility in conducting deep-dive evaluations of multi-class classifiers. By examining two different case scenarios, we illustrate how the choice of evaluation metrics can fundamentally shift the interpretation of a model's efficacy. Our findings emphasize that a multi-dimensional evaluation framework is essential for uncovering small but important differences in model performance. However, standard metrics may miss these subtle performance trade-offs.

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