Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.

Description: A general introduction about predictive modeling in insurance applications. Some basic models are discussed, such as Generalized Linear Model(GLM), Calssification and Regression Tree Model. Other models are also briefly introduced, including data clustering, neural networks and support vector machine.

Description: A book review by Scott McInturff of The Signal and the Noise: Why So Many Predictions Fail - But Some Don't authored by Nate Silver.

Description: A discussion on artificial intelligence.

Description: This article discussed the NEAT family of neural network algorithms. NEAT automatically determines the number of hidden neurons, as well as their structure. Additionally HyperNEAT can scale to very large problem domains.

Description: Overview of regularization and least angle regression approaches.

Description: Jeff Sagarin is one of the most well-known figures in the sports analytics world, with his sports ratings appearing in USA Today for over 30 years. Few may know that he started his career in the actuarial world, even passing three exams before his sports ratings took off. This article explores the parallels between the challenges of developing sports rating systems and the challenges that actuaries face every day.

Description: Chairperson's column for the July 2016 issue of Predictive Analytics & Futurism.

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.