Schedule of Actuarial Data Scientist Program: Fourth edition - Module 3 (16 CPD)

The aim of this session is to cover the local and global approaches to interpret output of a ML algorithm. We will focus on:

• Partial dependence plots
• Permutation feature importance
• Friedman’s interactions
• Global surrogate models
• Local Interpretable Model-Agnostic explanations (LIME)
• Shapley’s value (SHAP)

• At the surface, variable selection seems like a very straightforward and easy-to-understand concept, however, there is (much) more to it than meets the eye. That’s why we will start this session by clearly defining what variable selection is all about while facing the inherent ambiguity of the concept heads-on!
• Next, the usual suspects will be covered. This entails forward/backward/stepwise selection and penalized regression methods (mainly LASSO) but also how variable selection practically is hardwired into widely used ML models such as (ensemble) tree models and deep learning.
• The final part of the session will be about some more involved methods like Boruta and the Genetic Algorithm. For the latter method, a convenient adaptation of the well-known concordance probability (specifically tailored to the needs of insurance data) will be presented, with a special focus on its use in variable selection.

• Risks
• Biases
• Discriminations