Description: Collaborative filtering models drive recommendations provided by Netflix and Amazon. This article uses the same models to explore patients’ medical conditions.

Description: This paper describes techniques that can be used to access any specific piece of information in Wikipedia, such as medical information.

Description: An introduction of predictive modeling techniques that are applicable to insurance industry, including generalized linear model (GLM) and classification and regression tree model(CART). Other models such as data clustering, neural network, and support vector machine (SVM), are also briefly discussed.

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

Description: Summary of the first annual predictive analytics (PA) symposium specifically targeted to help actuaries get into PA & to take it to the next level of expertise.

Description: This article describes a method that can be useful when re-fitting a risk score model to a population of only moderate size. Learn how to use concepts like ridge regression to train a model with partial credibility.

Description: Process of building a predictive model and related considerations.

Description: The newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?

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

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.