# Evaluating and Calibrating Uncertainty Prediction in Regression Tasks Source: [Evaluating and Calibrating Uncertainty Prediction in Regression Tasks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:03:19.943Z Authors: Dan Levi, Liran Gispan, Niv Giladi, Ethan Fetaya Published: 2019-05-28T07:52:01.000Z ## Abstract Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications and in particular safety-critical ones. In this work we study the calibration of uncertainty prediction for regression tasks which often arise in real-world systems. We show that the existing definition for calibration of a regression uncertainty \[Kuleshov et al. 2018\] has severe limitations in distinguishing informative from non-informative uncertainty predictions. We propose a new definition that escapes this caveat and an evaluation method using a simple histogram-based approach. Our method clusters examples with similar uncertainty prediction and compares the prediction with the empirical uncertainty on these examples. We also propose a simple, scaling-based calibration method that preforms as well as much more complex ones. We show results on both a synthetic, controlled problem and on the object detection bounding-box regression task using the COCO and KITTI datasets. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()