Towards Data Science
Introduction to Deep Evidential Regression for Uncertainty Quantification
•1 min read•
#rag#llm#deployment#compute
Level:Intermediate
For:ML Engineers, Data Scientists
✦TL;DR
Deep Evidential Regression (DER) is a method that enables neural networks to quantify uncertainty in their predictions, allowing them to express what they don't know. This approach is significant because it can help mitigate the issue of overly confident models, leading to more reliable and trustworthy machine learning systems.
⚡ Key Takeaways
- Deep Evidential Regression (DER) is a technique for uncertainty quantification in neural networks
- DER allows models to express uncertainty in their predictions, reducing overconfidence
- This approach can lead to more reliable and trustworthy machine learning systems
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