{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/13"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"MAPIE computes prediction intervals and prediction sets for regression, classification, and time series, and controls risks for complex tasks like multi-label classification and semantic segmentation using conformal prediction methods.","skillfed_tags":["uncertainty-quantification","conformal-prediction","risk-control"],"use_cases":["Generate prediction intervals for regression models to quantify forecast uncertainty and communicate confidence bounds.","Compute prediction sets for classification to provide multiple plausible labels with statistical coverage guarantees.","Control false positive or false negative rates in high-stakes tasks by enforcing bounds on precision and recall.","Validate model reliability on new data by testing exchangeability assumptions before applying conformal methods.","Build uncertainty-aware computer vision pipelines for semantic segmentation with guaranteed metric bounds."],"what_it_does":"MAPIE is a scikit-learn-compatible library for quantifying uncertainty in machine learning predictions using conformal prediction and distribution-free inference methods. It computes prediction intervals for regression and classification tasks, as well as prediction sets for more complex scenarios like multi-label classification and image segmentation. The library implements peer-reviewed algorithms with theoretical guarantees under minimal assumptions.\n\nThe core workflow involves fitting a model on training data, then using a separate conformalization dataset to estimate prediction intervals or sets that provide probabilistic coverage guarantees. MAPIE also supports risk control, allowing you to set and enforce bounds on metrics like recall and precision. It depends on numpy, scikit-learn, and scipy, and is actively maintained with recent feature additions for emerging use cases.","worth_installing":"Yes. MAPIE is actively maintained, has low install friction, carries no known vulnerabilities, and provides a theoretically grounded approach to uncertainty quantification. It is worth installing if you need prediction intervals, prediction sets, or risk control for regression, classification, or computer vision tasks."},"id":"mapie","links":{"html":"https://skillfed.io/packages/mapie","md":"https://skillfed.io/packages/mapie.md","pypi":"https://pypi.org/project/mapie/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"MAPIE","python_support":"supports_current","summary":"A scikit-learn-compatible module for estimating prediction intervals."},"popularity":{"monthly_downloads":216153,"position":9386,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.0"}