Introduction to Deep Evidential Regression for Uncertainty Quantification

Towards Data Science / 4/16/2026

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Key Points

The article argues that machine learning models can appear confident even when they are uncertain, motivating methods for uncertainty quantification.
It introduces Deep Evidential Regression (DER) as an approach that enables neural networks to express uncertainty more directly and quickly.
DER is presented as a technique for producing probabilistic or evidential outputs that reflect what the model does not know.
The piece is positioned as an educational introduction to applying uncertainty-aware modeling rather than reporting a new system or product release.

Machine learning models can be confident even when they shouldn't be. This article introduces Deep Evidential Regression (DER), a method that lets neural networks rapidly express what they don't know.

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