Penalized Regression and Lasso Credibility
In this Akur8 Academy webinar, Thomas Holmes explores the power of penalized regression as an enhancement to traditional GLM (Generalized Linear Model) modeling. This session covers the theoretical and practical aspects of penalized regression, highlighting its deep connections to credibility theory. Additionally, Thomas introduces the concept of lasso credibility, demonstrating how penalized regression can be applied directly as a credibility procedure. By following the evolution from GLMs to lasso credibility, this webinar offers valuable insights and practical applications for actuaries and professionals in insurance modeling. If you missed this live session, request the full recording here!
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Penalized Regression and Lasso Credibility
In this Akur8 Academy webinar, Thomas Holmes explores the power of penalized regression as an enhancement to traditional GLM (Generalized Linear Model) modeling. This session covers the theoretical and practical aspects of penalized regression, highlighting its deep connections to credibility theory. Additionally, Thomas introduces the concept of lasso credibility, demonstrating how penalized regression can be applied directly as a credibility procedure. By following the evolution from GLMs to lasso credibility, this webinar offers valuable insights and practical applications for actuaries and professionals in insurance modeling. If you missed this live session, request the full recording here!
Learn more about the speakers
Thomas Holmes
Tom Holmes is Akur8’s Chief Actuary for the US region and is a co-author of the upcoming publication Penalized Regression and Lasso Credibility. He has experience modeling personal and commercial insurance, and volunteers with the CAS on predictive modeling topics. He is a frequent presenter at CAS events and Akur8 webinars, and performs industry outreach to share actuarial modeling methodologies and best practices. Tom is a Fellow of the CAS and holds music degrees from the University of Michigan and Ohio University.
