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: This article goes through some basic data exploration techniques to show how different approaches look.

Description: A discussion on how to use data visualization for model controls.

Description: Spreadsheets have become ubiquitous in business and forecasting. However, they do not usually have the same level of control that any other complex program would require. As a result, we often get erroneous results which can have far reaching, negative consequences.

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: Overview of regularization and least angle regression approaches.

Description: A review of the Bridging the Gap Series: Application of Predictive Modeling in VA/FIA Risk Management at the Equity Based Guarantees Conference in Chicago.

Description: The well-known examples of predictive modeling “successes” are numerous. Perhaps less widely recognized is the myriad of ways in which a predictive model can fail to perform as expected, often due to misconceptions or misrepresentations on the part of the analyst. This article, which is intended for a general actuarial audience, focuses on three such pitfalls. By understanding these three common pitfalls, readers may guard against them within their own work, teams, and organizations.

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: 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.