Conformal Prediction¶

Conformal prediction is a framework for quantifying the uncertainty of a model's predictions. Instead of returning only a point prediction, it uses the model's past errors to produce an output that expresses which values or labels remain plausible for a new observation.

MAPIE applies conformal prediction to any scikit-learn-compatible estimator:

for regression, it returns a prediction interval around each prediction;
for classification, it returns a set of plausible classes.

For example, with a confidence level of 90%, conformal prediction aims for the true value or class to be included in the predicted interval or set for at least 90% of future observations. This is a marginal coverage guarantee: it holds on average over new observations, not necessarily for every subgroup or individual observation.

Explore Conformal Prediction in MAPIE¶

Theory presents the foundational elements of conformal prediction.
Regression theory explains prediction intervals and the split, cross-validation, jackknife, and bootstrap-based methods.
Classification explains prediction sets and the split- and cross-conformal strategies.
Conformity scores describes how MAPIE measures whether a prediction agrees with an observation in regression and classification.
Conformalization set compares split- and cross-conformal workflows.
Metrics covers coverage, interval width, prediction-set size, and conditional coverage diagnostics.
Conditional guarantees introduces methods that go beyond marginal coverage.