Description: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.

Description: Anders discusses how skills in PA techniques don't have to become the focal point for all actuaries, but they can be another tool in the toolbox for all of us.

Description: Continuation of a tutorial on Fuzzy (vs. Boolean, or 'crisp') logic and how you can use it to create actuarial models that better fit the real world. The article provides basic theory in non-technical terms and shows applications using the R programming language.

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

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: 1. A simplified description of how a genetic algorithm works (requiring no knowledge of biology) and how to build your own. 2. A free Excel workbook that allows you to solve problems without doing any coding; or to let you extend the existing code for your own special needs.

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

Description: 3rd Annual iPad contest announcement

Description: This article introduces unsupervised methods: modeling methods that do not depend on picking an independent variable and dependent variable. Such methods are used, among other things, for determining how entities are clustered.