Upcoming Meetings

• June 9, 2026 from 14.00 to 17.00
• VU Amsterdam room NU-4B47 (a link to join online will be provided later.)

Beyond Coefficients Understanding Variable Importance in Modern ML by Ángel Reyero Lobo (CWI-Inria-IMT)

Classical statistical models offer straightforward interpretations through their coefficients, but modern machine learning models often behave as black boxes. This talk explores how variable importance methods evolved to bridge this interpretability gap. After discussing Random Forest importance measures and the emergence of model-agnostic permutation methods, we focus on the challenges posed by correlated variables and conditional dependencies. We present Conditional Feature Importance (CFI) and Leave One Covariate Out (LOCO) as principled alternatives.

Take your computer with you for some hands-on experience!