arXiv:2510.25573v1 Announce Type: cross 
Abstract: Machine learning approaches for image classification have led to impressive advances in that field. For example, convolutional neural networks are able to achieve remarkable image classification accuracy across a wide range of applications in industry, defense, and other areas. While these machine learning models boast impressive accuracy, a related concern is how to assess and maintain calibration in the predictions these models make. A classification model is said to be well calibrated if its predicted probabilities correspond with the rates events actually occur. While there are many available methods to assess machine learning calibration and recalibrate faulty predictions, less effort has been spent on developing approaches that continually monitor predictive models for potential loss of calibration as time passes. We propose a cumulative sum-based approach with dynamic limits that enable detection of miscalibration in both traditional process monitoring and concept drift applications. This enables early detection of operational context changes that impact image classification performance in the field. The proposed chart can be used broadly in any situation where the user needs to monitor probability predictions over time for potential lapses in calibration. Importantly, our method operates on probability predictions and event outcomes and does not require under-the-hood access to the machine learning model.

أدت التطورات الأخيرة في التعلم الآلي، وخاصة من خلال الشبكات العصبية التلافيفية، إلى تحسين كبير في دقة تصنيف الصور عبر مختلف القطاعات. ومع ذلك، مع تزايد انتشار هذه النماذج، يصبح من الضروري ضمان معايرة توقعاتها. يناقش هذا المقال أهمية مراقبة توقعات الاحتمالات ويقدم طرقًا لاكتشاف انحراف المفهوم، مما يمكن أن يساعد في الحفاظ على موثوقية هذه النماذج في التطبيقات الواقعية. هذا أمر حيوي للصناعات التي تعتمد على تصنيف الصور بدقة، حيث يؤثر بشكل مباشر على اتخاذ القرار وكفاءة العمليات.

Los recientes avances en el aprendizaje automático, especialmente a través de redes neuronales convolucionales, han mejorado significativamente la precisión de la clasificación de imágenes en varios sectores. Sin embargo, a medida que estos modelos se vuelven más comunes, asegurar la calibración de sus predicciones es crucial. Este artículo discute la importancia de monitorear las previsiones de probabilidad e introduce métodos para detectar el cambio de concepto, lo que puede ayudar a mantener la fiabilidad de estos modelos en aplicaciones del mundo real. Esto es vital para las industrias que dependen de una clasificación de imágenes precisa, ya que impacta directamente en la toma de decisiones y la eficiencia operativa.

Les récentes avancées en apprentissage automatique, notamment grâce aux réseaux de neurones convolutionnels, ont considérablement amélioré la précision de la classification d'images dans divers secteurs. Cependant, à mesure que ces modèles deviennent plus courants, il est crucial d'assurer la calibration de leurs prédictions. Cet article aborde l'importance de surveiller les prévisions de probabilité et introduit des méthodes pour détecter le dérive conceptuelle, ce qui peut aider à maintenir la fiabilité de ces modèles dans des applications réelles. Cela est vital pour les industries qui dépendent d'une classification d'images précise, car cela impacte directement la prise de décision et l'efficacité opérationnelle.

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 help maintain the reliability of these models in real-world applications. This is vital for industries relying on accurate image classification, as it directly impacts decision-making and operational efficiency.

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

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

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