Louis Lacombe
Louis Lacombe, Data Scientist at Capgemini Invent, France.
Louis Lacombe, a graduate of the MSc program at the University of Bocconi, joined Quantmetry (now Capgemini Invent) in 2022. He began his journey in conformal predictions with the implementation of conformalized quantile regression in the MAPIE library.
Currently, he is one of the core developers of the library and focuses on enhancing its visibility by demonstrating diverse use cases and by integrating conformal predictions into clients' algorithms and advocating for a trusted AI approach.
Session
MAPIE (Model Agnostic Prediction Interval Estimator) is your go-to solution for managing uncertainties and risks in machine learning models. This Python library, nestled within scikit-learn-contrib, offers a way to calculate prediction intervals with controlled coverage rates for regression, classification, and even time series analysis. But it doesn't stop there - MAPIE can also be used to handle more complex tasks like multi-label classification and semantic segmentation in computer vision, ensuring probabilistic guarantees on crucial metrics like recall and precision. MAPIE can be integrated with any model - whether it's scikit-learn, TensorFlow, or PyTorch. Join us as we delve into the world of conformal predictions and how to quickly manage your uncertainties using MAPIE.
Link to Github: https://github.com/scikit-learn-contrib/MAPIE