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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.01973 |
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Table of Contents:
- Evaluating the performance of classifiers is critical in machine learning, particularly in high-stakes applications where the reliability of predictions can significantly impact decision-making. Traditional performance measures, such as accuracy and F-score, often fail to account for the uncertainty inherent in classifier predictions, leading to potentially misleading assessments. This paper introduces the Certainty Ratio ($C_ρ$), a novel metric designed to quantify the contribution of confident (certain) versus uncertain predictions to any classification performance measure. By integrating the Probabilistic Confusion Matrix ($CM^\star$) and decomposing predictions into certainty and uncertainty components, $C_ρ$ provides a more comprehensive evaluation of classifier reliability. Experimental results across 21 datasets and multiple classifiers, including Decision Trees, Naive-Bayes, 3-Nearest Neighbors, and Random Forests, demonstrate that $C_ρ$ reveals critical insights that conventional metrics often overlook. These findings emphasize the importance of incorporating probabilistic information into classifier evaluation, offering a robust tool for researchers and practitioners seeking to improve model trustworthiness in complex environments.