fregu856/regression_uncertainty
Official implementation of "How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?", TMLR 2023.
This project helps machine learning researchers and practitioners understand how reliable their regression models are when encountering new, unexpected data. It takes common image-based regression datasets and evaluates various uncertainty estimation methods to see if they accurately reflect when the model is unsure about its predictions, especially under different real-world scenarios. The output is a benchmark showing which methods remain trustworthy even when the data changes.
No commits in the last 6 months.
Use this if you are developing or using regression models that need to provide reliable uncertainty estimates, particularly when deployed in environments where data characteristics might shift over time, such as in medical imaging or industrial inspection.
Not ideal if you are looking for a plug-and-play solution for a specific application without delving into the underlying research and model evaluation.
Stars
18
Forks
1
Language
Python
License
MIT
Last pushed
Mar 21, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/fregu856/regression_uncertainty"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
EmuKit/emukit
A Python-based toolbox of various methods in decision making, uncertainty quantification and...
google/uncertainty-baselines
High-quality implementations of standard and SOTA methods on a variety of tasks.
nielstron/quantulum3
Library for unit extraction - fork of quantulum for python3
IBM/UQ360
Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you...
aamini/evidential-deep-learning
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!