EuroSciPy 2024

Conformal Prediction with MAPIE: A Journey into Reliable Uncertainty Quantification
08-28, 14:25–14:45 (Europe/Berlin), Room 6

In the ever-evolving landscape of data science, accurate uncertainty quantification is crucial for decision-making processes. Conformal Prediction (CP) stands out as a powerful framework for addressing this challenge by providing reliable uncertainty estimates alongside predictions. In this talk, I'll delve into the world of Conformal Prediction, with a focus on the MAPIE Python library, offering a comprehensive understanding of its advantages and practical applications.


-Advantages and Fundamentals concepts of Conformal Prediction
Uncertainty is an inherent aspect of real-world data, and accurate quantification is vital for making informed decisions. Conformal Prediction offers a principled approach to estimate the uncertainty associated with predictions, providing users with more reliable and actionable insights.

-Types of conformal predictors
Not all conformal predictors are created equal. I'll give an introduction of different types of CP predictors.

-MAPIE python library
I'll present MAPIE (Model Agnostic Prediction Interval Estimator), a Python library that simplifies the implementation of Conformal Prediction.

-Practical example on tabular data
To bring theory into practice, I'll walk through an use case using tabular data.


Abstract as a tweet

Navigating Uncertainty: Conformal Prediction with MAPIE

Category [Data Science and Visualization]

Statistics

Expected audience expertise: Domain

some

Expected audience expertise: Python

some

Public link to supporting material

https://github.com/claudio1975/CO2_Emissions

I'm an actuary moving towards a freelance data science job.
I was a trainee in several SDS & SwissText conference editions.
I was a speaker in several Insurance Data Science Conference editions and meetups (Zurich & Munich).
I was an assistant professor for Insurance Statistics at the Catholic University of Milan.
I started my data science journey with kaggle and hackathons.

This speaker also appears in: