2024 ValAct Meeting - Session 2D: Experience Study with GAM and CatBoost in Python 8.26.24 Recording

In this session, we will delve into the powerful capabilities of two essential algorithms in the field of data science and machine learning: Generalized Additive Models (GAM) and CatBoost. These algorithms can play a crucial role in conducting experience studies for insurance. The session will begin with an introduction to GAM and CatBoost. During this segment, participants will gain a solid understanding of the fundamental concepts behind the two model algorithms. We'll then delve into the advantages and strengths of these algorithms; particularly in model interpretability, flexibility to capture non-normal distribution, risk assessment and management, interactive feature importance and effective handling of categorical variables. Finally, the session will end with a walkthrough of practical examples using Pygam and CatBoost Python libraries. By attending this session, participants will not only grasp the concepts behind GAM and CatBoost, but also acquire the skills to use them effectively in experience studies