Description: In this article, we will overview some popular tree-based models and understand how such models may be applied to the insurance industry.

Description: In An Introduction to Incremental Learning, by Qiang Wu and I describe how machine learning is a natural enhancement to predictive analytics through its many useful tools to derive meaning from a lot of otherwise unusable data such as handwriting. Again, there are formulas involved; but we have tried to keep them understandable to the actuary who isn’t already familiar with stochastic gradient descent, perceptrons, and principal component analysis. Incremental learning can help you refine the estimations you derive from more traditional actuarial methods.

Description: The article introduces the concept of overfitting in predictive modeling and illustrates how it hinders our ability to predict new data well.

Description: This article discusses techniques used to create risk scores to help determine appropriate primary care panel sizes for a provider organization.

Description: Looks at free and inexpensive resources for the novice in predictive analytics, covering the data analysis theory as well as the most commonly used languages/software used in the field.

Description: In this article, we are going to discuss the AR model by making connections to time-dependent ordinary differential equations.

Description: Summary of what the section has accomplished over the past year.

Description: Why Actuaries Should Welcome Automation

Description: Reviewing a text covering multiple statistical modeling topics: e.g., generalized linear models, decision trees, clustering, using the statistical language R.

Description: History of Big Data, and approaches to cope with it. The author suggests that actuaries have been using similar methods for a long time; but need to get up to speed on the newer terms and variations.