Pedro Albuquerque - Generalized Additive Models: Explainability Strikes Back | PyData Seattle 2025 скачать в хорошем качестве

Pedro Albuquerque - Generalized Additive Models: Explainability Strikes Back | PyData Seattle 2025

Generalized Additive Models (GAMs) strike a rare balance: they combine the flexibility of complex models with the clarity of simple ones. They often achieve performance comparable to black-box models, yet remain: Easy to interpret Computationally efficient Aligned with the growing demand for transparency in AI With recent U.S. AI regulations (White House, 2022) and increasing pressure from decision-makers for explainable models, GAMs are emerging as a natural choice across industries. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...