Machine learning (ML) models are powerful tools of prediction but suffer from a lack of interpretability.
The aim of this course is to introduce the local and global methods analyzing relations between output and input of complex ML algorithms.
The module is illustrated with examples in Python provided to participants.
Partial dependence plots
Permutation feature importance
Friedman’s interactions
Global surrogate models
Local Interpretable Model-Agnostic explanations (LIME)
Shapley’s value (SHAP)
Scientific Advisor, Detralytics
Professor, UCLouvain
Date : On-Demand
Duration : 3h
Accreditation : 3CPD | 18PPC
Level : All
At the end of the training, participants will be able to:
Donatien Hainaut is a Scientific Advisor at Detralytics and a professor at UCLouvain (Belgium), where he serves as the Director of the Master’s program in Data Science with a statistical orientation. Prior to this, he held several academic positions, including Associate Professor at Rennes School of Business and ENSAE in Paris. He also has extensive industry experience, having worked as a Risk Officer, Quantitative Analyst, and ALM Officer.
Donatien is a Qualified Actuary and holds a PhD in the field of Asset and Liability Management. His current research focuses on contagion mechanisms in stochastic processes and the applications of neural networks in insurance.
