Derivative Lasso is a cutting edge machine learning technique that seamlessly merges actuarial credibility, robustness and interpretability into a transformative actuarial pricing tool. Where traditional GLMs are viewed as highly manual due to feature engineering being an overly iterative process, Derivative Lasso advances the field, embedding this process directly within its core. Using real-world data, this session will spotlight the challenges in current GLM modeling and unveil the power and precision of the Derivative Lasso framework. Attendees will discover how it automates feature engineering, fortifies model robustness, and elevates interpretability, marking a significant leap in penalized regression modeling that keeps GLMs on par with newer modeling frameworks.
Learning Objectives:
Describe the typical pitfalls and the challenges associated with feature engineering in traditional loss modeling.
Understand the derivative lasso methodology and the changes in assumptions from traditional penalized regression.
Identify the situations in which the derivative lasso framework can be highly beneficial to model building.
Describe the typical pitfalls and the challenges associated with feature engineering in traditional loss modeling.
