2024 Webinar - Derivative Lasso:
Credibility-based signal fitting for GLMs
- June 13
Derivative Lasso is a cutting edge machine learning technique that seamlessly merges actuarial credibility, robustness and interpretability into a transformative actuarial pricing tool. This method is consistent with the Lasso Credibility concept covered in the upcoming CAS monograph. 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:
- Evaluate the limitations of traditional GLMs in terms of manual feature engineering and iterative processes.
- Apply the Derivative Lasso technique to automate feature engineering while maintaining model robustness and interpretability.
- Compare the performance and efficiency of Derivative Lasso to traditional GLMs, focusing on improvements in automation, robustness, and interpretability.
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