2024-08-28 –, 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.
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:
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.