Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models

ArXi:2508.17761v3 Announce Type: replace In safety-critical applications data-driven models must not only be accurate but also provide reliable uncertainty estimates. This property, commonly referred to as calibration, is essential for risk-aware decision-making. In regression a wide variety of calibration metrics and recalibration methods have emerged. However, these metrics differ significantly in their definitions, assumptions and scales, making it difficult to interpret and compare results across studies.