Description: Overview of regularization and least angle regression approaches.

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

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

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

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.

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