Ressources

PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...). PyXAI generates explanations that are post-hoc and local. In contrast to many popular approaches to XAI (SHAP, LIME, ...), PyXAI generates explanations that are also correct. Being correct (aka sound or faithful) indicates that the explanations that are provided actually reflect the exact behaviour of the model by guaranteeing certain properties about the explanations generated.

PyCSP3 is a Python library for developping models of combinatorial constrained problems in a declarative manner; you can write models of constraint satisfaction (CSP) and constraint optimization (COP) problems. In this website, you will find all that you need to know about Constraint Programming (CP) and PyCSP3, with more than 60 Jupyter Notebooks: - find the 25 popular constraints , with dedicated Jupyter Notebooks - discover, step by step, more than 30 models of classical problems (Sport Scheduling, Social Golfers, Warehouse Location , ...), with dedicated Jupyter Notebooks You can also: - read this well-documented guide - explore a GitHub repository containing more than 340 models at pycsp3-models https://pycsp.org/

PyImageLabeling is a powerful tool with a user-friendly interface based on PyQT6 for creating image masks. These labeled images are used in the creation of machine learning models dedicated to computer vision tasks.