Description: Using the Cox proportional Hazards Model to improve underwriting debits and credits.

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: This article compares three potential approaches to determining new coefficients for health-based risk scoring models: full recalibration, p-value credibility weighting, and penalized ridge regression.

Description: This article explains how to create more maintainable models using abstractions by relating it to option, machine learning and machine intelligence models.

Description: This article is a basic introduction to information theory with the goal of explaining the maximal information coefficient.

Description: Specifies 6 rules which help shape not only good crime-scene analysis, but good futures practice, as well. These rules address project management and control.

Description: An introduction to Hidden Markov Models, which are used extensively in speech and facial recognition technology. They are a useful tool in forecasting time series and in developing categorizations of data based on the values of the data themselves.

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: 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?