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 look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

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: An introduction to creating Shiny web applications, with sample code available to reproduce the interactive application displayed in screenshots in the article.

Description: Introduction to PMML and how to use of it in the R programming language. PMML is an interchange format for predictive models.

Description: This is an Editorial describing the need for actuaries to upgrade skills or risk minimization from artificial intelligence.

Description: The author reviews a book the debunks some of the hype we read about using quantity at the expense of quality. She also points out some unrealistic outcomes when data scientists lack business knowledge.

Description: Editorial for the May 2019 issue of Predictive Analytics & Futurism newsletter.

Description: Read the February 2020 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section. Newsletter provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

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