yromano/cqr
Conformalized Quantile Regression
This helps data scientists, machine learning engineers, and researchers build more trustworthy predictive models by providing clear prediction intervals. It takes existing regression models and data, then outputs a range of predicted values that comes with a guaranteed level of certainty. This is useful when communicating the reliability of AI recommendations to decision-makers.
304 stars.
Use this if you need to quantify the uncertainty of your predictions and ensure fairness across different groups in your data.
Not ideal if you are looking for a standalone predictive model rather than a method to improve the reliability of an existing one.
Stars
304
Forks
54
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 02, 2026
Commits (30d)
0
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