mlsanityData to 7 Oct 2026

MAPIE: an open-source library for distribution-free uncertainty quantification

arXiv 2207.12274 ↗First posted 25 Jul 2022stat.ML cs.LGcited by 17

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Abstract (arXiv)

Estimating uncertainties associated with the predictions of Machine Learning (ML) models is of crucial importance to assess their robustness and predictive power. In this submission, we introduce MAPIE (Model Agnostic Prediction Interval Estimator), an open-source Python library that quantifies the uncertainties of ML models for single-output regression and multi-class classification tasks. MAPIE implements conformal prediction methods, allowing the user to easily compute uncertainties with strong theoretical guarantees on the marginal coverages and with mild assumptions on the model or on the underlying data distribution. MAPIE is hosted on scikit-learn-contrib and is fully "scikit-learn-compatible". As such, it accepts any type of regressor or classifier coming with a scikit-learn API. The library is available at: https://github.com/scikit-learn-contrib/MAPIE/.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 25 Jul 2022.

Builds on

12 of 19 references.

References · 12

  1. 2019Conformalized Quantile Regressioncited by 620
  2. 2019Conformal Prediction Under Covariate Shiftcited by 511
  3. 2020Classification with Valid and Adaptive Coveragecited by 390
  4. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
  5. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 360
  6. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 278
  7. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 270
  8. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
  9. 2019Predictive inference with the jackknife+cited by 206
  10. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 99
  11. 2020Predictive Inference Is Free with the Jackknife+-after-Bootstrapcited by 55
  12. 2021Copula-based conformal prediction for Multi-Target Regressioncited by 53

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 4; 2026: 4.

17 papers.

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  1. 2025Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Predictioncited by 26
  2. 2024Interpretable Machine Learning for TabPFNcited by 10
  3. 2024Uncertainty quantification for probabilistic machine learning in earth observation using conformal predictioncited by 9
  4. 2025Towards Fair In-Context Learning with Tabular Foundation Modelscited by 8
  5. 2024Conformal time series decomposition with component-wise exchangeabilitycited by 3
  6. 2025On the Performance of LLMs for Real Estate Appraisalcited by 3
  7. 2024From Conformal Predictions to Confidence Regionscited by 2
  8. 2023From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modelingcited by 1
  9. 2024Adaptive Conformal Inference for Multi-Step Ahead Time-Series Forecasting Onlinecited by 1
  10. 2025Reliable Statistical Guarantees for Conformal Predictors with Small Datasetscited by 1
  11. 2024Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulationscited by 0
  12. 2024Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Predictioncited by 0
5 more
  1. 2024Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimizationcited by 0
  2. 2026Flood Risk Follows Valleys, Not Grids: Graph Neural Networks for Flash Flood Susceptibility Mapping in Himachal Pradesh with Conformal Uncertainty Quantificationcited by 0
  3. 2026Accurate and Reliable Uncertainty Estimates for Deterministic Predictions Extensions to Under and Overpredictionscited by 0
  4. 2026Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Studycited by 0
  5. 2026Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negativecited by 0