The Toolbox for Rating Diagnostic Tests: A Guide to Classification Metrics

Abstract

Evaluating a classifier's performance is critical for its successful application. This paper explores various metrics used for binary classification tasks, highlighting their strengths and limitations. Simple threshold metrics, such as Accuracy and Sensitivity, are efficient for binary data and a single cutoff point. However, their reliance on a single threshold and sensitivity to imbalanced data can be drawbacks. For more robust evaluation, ranking metrics such as Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves provide a threshold-agnostic approach, enabling comparison across different cutoff points. Additionally, probabilistic metrics like Brier Score and Log Loss assess the model's ability to predict class probabilities. The choice of metric depends on the specific classification problem and the characteristics of the data. When dealing with imbalanced data or complex decision-making processes, using multiple metrics is recommended to gain a comprehensive understanding of the model's performance. This paper emphasises the importance of understanding metric limitations and of selecting appropriate metrics for a specific classification task. By doing so, researchers and practitioners can ensure a more accurate and informative evaluation of their models, ultimately leading to the development of reliable tools for various applications.