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Monitoring the calibration of probability forecasts with an application to concept drift detection involving image classification

arXiv — stat.ML•Thursday, October 30, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
Recent advancements in machine learning, particularly through convolutional neural networks, have significantly improved image classification accuracy across various sectors. However, as these models become more prevalent, ensuring the calibration of their predictions is crucial. This article discusses the importance of monitoring probability forecasts and introduces methods for detecting concept drift, which can impact the reliability of these models. Understanding and maintaining calibration not only enhances model performance but also builds trust in AI applications, making this research highly relevant in today's tech-driven landscape.
— Curated by the World Pulse Now AI Editorial System
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