The Certainty Ratio $C_ρ$: a novel metric for assessing the reliability of classifier predictions

• URL: http://arxiv.org/abs/2411.01973v1
• Date: Mon, 04 Nov 2024 10:50:03 GMT
• Title: The Certainty Ratio $C_ρ$: a novel metric for assessing the reliability of classifier predictions
• Authors: Jesus S. Aguilar-Ruiz,
• Abstract summary: This paper introduces the Certainty Ratio ($C_rho$), a novel metric designed to quantify the contribution of confident (certain) versus uncertain predictions to any classification performance measure. Experimental results across 26 datasets and multiple classifiers, including Decision Trees, Naive-Bayes, 3-Nearest Neighbors, and Random Forests, demonstrate that $C_rho$rho reveals critical insights that conventional metrics often overlook.
• Score: 0.0
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• Abstract: 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 ($C_\rho$), 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, $C_\rho$ provides a more comprehensive evaluation of classifier reliability. Experimental results across 26 datasets and multiple classifiers, including Decision Trees, Naive-Bayes, 3-Nearest Neighbors, and Random Forests, demonstrate that $C_\rho$ 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.

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