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 purpose of regularization in actuarial pricing mdoels
Outline the complications with feature engineering and p-values in traditional pricing models
Describe the derivative lasso technique and its benefits
