Machine learning classifiers are probabilistic in nature, and thus inevitably involve uncertainty. Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management. Post-hoc model calibrations can improve models' uncertainty estimations without the need for retraining, and without changing the model. Our work puts forward a geometric-based approach for uncertainty estimation. Roughly speaking, we use the geometric distance of the current input from the existing training inputs as a signal for estimating uncertainty and then calibrate that signal (instead of the model's estimation) using standard post-hoc calibration techniques. We show that our method yields better uncertainty estimations than recently proposed approaches by extensively evaluating multiple datasets and models. In addition, we also demonstrate the possibility of performing our approach in near real-time applications. Our code is available at our Github https://github.com/NoSleepDeveloper/Geometric-Calibrator.

翻译：机械学习分类器具有概率性,因此不可避免地涉及不确定性。预测某项具体投入正确的可能性被称为不确定性(或信心)估计,对于风险管理至关重要。 热后模型校准可以改进模型的不确定性估计,而不需要再培训,也不改变模型。 我们的工作提出了一种基于几何的不确定性估计方法。 粗略地说,我们使用现有培训投入中当前投入的几何距离作为估计不确定性的信号,然后使用标准的后热校准技术校准该信号(而不是模型的估计)。 我们通过广泛评估多个数据集和模型,表明我们的方法比最近提出的方法产生更好的不确定性估计。 此外,我们还展示了在近实时应用中执行我们的方法的可能性。我们的代码可以在我们的Github https://github.com/NoSlicdevelop Developer/Geodegraphic-Calibrator上查阅。