MAPIE
A scikit-learn-compatible module for estimating prediction intervals.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10; scikit-learn >=1.4 and numpy >=1.23 must be installed.
- Low friction installation with a pure Python wheel.
- Actively maintained with a release 9 days old and recent commits.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive; you can use MAPIE in commercial and proprietary projects with minimal restrictions beyond retaining the license notice.
last release 2026-08-05 (9 days) · last repo commit 2026-08-14 · 1,579 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 216,153 downloads/mo, #9,386 on PyPI
Alternatives
Verify before relying
pip install mapie
from mapie.regression import MapieRegressor
model = MapieRegressor(base_estimator=None)
model.fit(X_train, y_train)
y_pred, y_pi = model.predict(X_test)- Whether the library's theoretical guarantees hold for all data distributions or only under specific exchangeability assumptions.
- Performance overhead of conformal prediction methods on large datasets compared to point predictions alone.
- Compatibility with TensorFlow and PyTorch models beyond scikit-learn wrappers.
What it is and 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
mapie on PyPI
Before you install
Low friction installation with a pure Python wheel. Actively maintained with a release 9 days old and recent commits. Depends on three standard scientific packages: numpy, scikit-learn, and scipy.
Requires Python >=3.10; scikit-learn >=1.4 and numpy >=1.23 must be installed.
License in practice
BSD-3-Clause is permissive; you can use MAPIE in commercial and proprietary projects with minimal restrictions beyond retaining the license notice.
Quickstart
pip install mapie
from mapie.regression import MapieRegressor
model = MapieRegressor(base_estimator=None)
model.fit(X_train, y_train)
y_pred, y_pi = model.predict(X_test)Verify before relying
- Whether the library's theoretical guarantees hold for all data distributions or only under specific exchangeability assumptions.
- Performance overhead of conformal prediction methods on large datasets compared to point predictions alone.
- Compatibility with TensorFlow and PyTorch models beyond scikit-learn wrappers.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpyscikit-learnscipy |
| Maintenance | Actively maintained 9 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 216,153 / month, #9,386 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: mapie-1.5.0-py3-none-any.whl
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