Online-cp : a Python Package for Online Conformal Prediction, Conformal Predictive Systems and Conformal Test Martingales

• Conformal prediction (CP) has gained increasing attention in machine learning owing to its ability to provide reliable prediction sets with well-calibrated uncertainty estimates. While most existing CP implementations focus on inductive conformal prediction (ICP), full conformal prediction—also known as online or transductive CP—offers the strongest validity guarantees but has been largely absent from open-source software due to its computational complexity. In this paper, we introduce online-cp, a Python package designed for online conformal prediction, conformal predictive systems (CPS), and conformal test martingales. The package implements several online CP algorithms, enabling efficient and principled uncertainty quantification in streaming data scenarios. Additionally, it includes tools for testing the exchangeability assumption by using conformal test martingales. We demonstrate the functionality of online-cp through classification and regression examples as well as applications to predictive systems and exchangeability testing. By making online CP methods accessible, online-cp provides a foundation for the broader adoption and further development of conformal prediction in real-time machine learning applications. 

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

Conformal predictive systems

Conformal test martingales

Exchangeability testing

Machine learning

Online conformal prediction

Python package

Uncertainty quantification

E-learning

Forecasting

Interactive computer systems

Online systems

Open source software

Open systems

Python

Software testing

Uncertainty analysis

Well testing

Conformal predictions

Conformal predictive system

Conformal test martingale

Machine-learning

Predictive systems

Uncertainty estimates

Uncertainty quantifications

Learning systems

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