Boosting AI Reliability: Uncertainty Quantification with MAPIE
Introduction
Thierry Cordier and Valentin Laurent introduce MAPIE, a Python library within scikit-learn-contrib, designed for uncertainty quantification in machine learning models.
Managing Uncertainty in Machine Learning
In AI applications — from autonomous vehicles to medical diagnostics — understanding prediction uncertainty is crucial. MAPIE uses conformal prediction methods to generate prediction intervals with controlled confidence, ensuring safer and more interpretable AI systems.
Key Features
MAPIE supports regression, classification, time series forecasting, and complex tasks like multi-label classification and semantic segmentation. It integrates seamlessly with scikit-learn, TensorFlow, PyTorch, and custom models.
Real-World Use Cases
By generating calibrated prediction intervals, MAPIE enables selective classification, robust decision-making under uncertainty, and provides statistical guarantees critical for safety-critical AI systems.
Conclusion
MAPIE empowers data scientists to quantify uncertainty elegantly, bridging the gap between predictive power and real-world reliability.