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Pre-Recorded Talk
Workshop: Workshop on Distribution-Free Uncertainty Quantification

MAPIE: Model Agnostic Prediction Interval Estimator (Spotlight #12)

Vianney Taquet · Gregoire Martinon · Nicolas J-B Brunel


Estimating uncertainties associated with the predictions of machine learning models is of crucial importance to assess their robustness and predictive power. Recently, new distribution-free methods have emerged and allow to compute uncertainties with strong theoretical guarantees without making any assumption on the model nor on the underlying data distribution. In this paper, we introduce MAPIE (Model Agnostic Prediction Interval Estimator), an open-source python package following the standard scikit-learn API and implementing recent resampling methods such as the jackknife+. The package is available at this url: