Conformal Prediction in Python: Trustworthy ML Intervals

Your model predicts a house will sell for $420,000. Should the buyer trust that number to the dollar? Almost never. A single point prediction hides how unsure the model actually is, and in 2026 — with ML quietly driving pricing engines, clinical triage, and automated lending — “how confident are you?” has turned into a compliance question rather than a nice-to-have.

Conformal prediction answers it with a guarantee. Instead of one number, your model returns a range that contains the true value a chosen fraction of the time — say 90%. The surprising part: that guarantee holds for any model, with no assumption that your errors are Gaussian, and without retraining anything. This guide shows you how to bolt that guarantee onto an existing scikit-learn model using MAPIE (Model Agnostic Prediction Interval Estimator), whose redesigned v1 API is now the standard way to do conformal prediction in Python.