Detecting Concept Drift With Neural Network Model Uncertainty

Understand

Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift.

• While existing approaches of concept drift detection already show convincing results, they require true labels as a prerequisite for successful drift detection.
• Especially in many real-world application scenarios-like the ones covered in this work-true labels are scarce, and their acquisition is expensive.
• Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift Detection (UDD), which is able to detect drifts without access to true labels.