scikit-learn-contrib/MAPIE

Health v2 · maintenance only

Adoption not part of the score

1596 stars · 155 forks observed · 2026-09-28

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

MAPIE is a scikit-learn-compatible Python library for quantifying uncertainty in machine learning models via conformal prediction. It computes prediction intervals and prediction sets for regression, classification, and time series, and provides risk-control mechanisms.

Use cases

• estimate prediction intervals for regression models
• compute prediction sets for classification with coverage guarantees
• quantify uncertainty in time series forecasts
• control false positive rates of ML models
• apply risk control to LLM-as-Judge evaluations
• verify exchangeability before applying conformal prediction

When to choose

• you use scikit-learn and need calibrated uncertainty estimates
• you need statistical coverage guarantees for predictions
• you want model-agnostic conformal prediction methods

When to avoid

• you need Bayesian uncertainty methods rather than conformal ones
• your model or data violates exchangeability assumptions
• you work outside the Python/scikit-learn ecosystem

Facets

library · maturity active

machine-learning testing machine-learning data-science python conformal-prediction prediction-intervals uncertainty-quantification risk-control scikit-learn-compatible

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markdown · JSON · MCP: product_card(name="scikit-learn-contrib/MAPIE")