aangelopoulos/conformal-prediction resource

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1081 stars · 123 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks demonstrating conformal prediction (conformal inference) on real-world machine learning tasks like image classification, regression, and time series. It serves as both a practical tutorial and a template sandbox for developing uncertainty quantification methods without needing to run the underlying models.

Use cases

• learn conformal prediction with real examples
• build prediction sets with coverage guarantees
• quantify uncertainty in machine learning models
• apply conformalized quantile regression for prediction intervals
• experiment with uncertainty estimation under distribution shift
• prototype new conformal prediction methods

When to choose

• you want to learn or teach conformal prediction hands-on
• you need statistically guaranteed prediction sets or intervals
• you want notebook templates for uncertainty quantification research

When to avoid

• you need a production-ready conformal prediction library with an API
• you want a maintained pip-installable package rather than notebooks
• your task is unrelated to statistical uncertainty estimation

Facets

learning-resource · maturity active

machine-learning data-science nlp computer-vision machine-learning data-science tutorials python conformal-prediction uncertainty-quantification jupyter-notebooks prediction-sets distribution-shift time-series-forecasting statistics

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