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[2505.05295] Performance Estimation in Binary Classification Using Calibrated Confidence
Summary
This article presents a novel method, CBPE, for estimating binary classification metrics without requiring ground truth labels, enhancing model performance monitoring in machine learning.
Why It Matters
The ability to estimate performance metrics without ground truth labels addresses a significant challenge in machine learning, particularly in real-world applications where such labels may not be available. This method can improve model monitoring and reliability, making it crucial for practitioners and researchers in the field.
Key Takeaways
- CBPE estimates binary classification metrics using calibrated confidence scores.
- It provides strong theoretical guarantees and valid confidence intervals for performance estimates.
- The method addresses the gap in performance monitoring without ground truth labels.
- Four key metrics—accuracy, precision, recall, and F1—are demonstrated using CBPE.
- This approach enhances the reliability of model performance assessments in deployment.
Computer Science > Machine Learning arXiv:2505.05295 (cs) [Submitted on 8 May 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Performance Estimation in Binary Classification Using Calibrated Confidence Authors:Juhani Kivimäki, Jakub Białek, Wojtek Kuberski, Jukka K. Nurminen View a PDF of the paper titled Performance Estimation in Binary Classification Using Calibrated Confidence, by Juhani Kivim\"aki and 3 other authors View PDF HTML (experimental) Abstract:Model monitoring is a critical component of the machine learning lifecycle, safeguarding against undetected drops in the model's performance after deployment. Traditionally, performance monitoring has required access to ground truth labels, which are not always readily available. This can result in unacceptable latency or render performance monitoring altogether impossible. Recently, methods designed to estimate the accuracy of classifier models without access to labels have shown promising results. However, there are various other metrics that might be more suitable for assessing model performance in many cases. Until now, none of these important metrics has received similar interest from the scientific community. In this work, we address this gap by presenting CBPE, a novel method that can estimate any binary classification metric defined using the confusion matrix. In particular, we choose four metrics from this large family: accuracy, precision, recall, and F$_1$, to demonstrate our method. CBPE treat...
