Machine Learning applications to non-life pricing Frequency modeling: An educational case study Machine Learning techniques operate data to perform classification and prediction. They are becoming more and more popular, due (among other things) to the still increasing computational power of machines. These algorithms use a very limited set of assumptions, compared to “standard” statistical methods. However, on the contrary of such methods, inference is not the main goal of Machine Learning techniques: they focus on classification and prediction. This is the reason why these “innovative” techniques are considered as more “black box” solutions. This paper presents a comparison on a simulated database between traditional statistical predictive modeling techniques (GLM and GAM), machine learning techniques (regression trees, bagging, random forests, boosting and neural networks) and penalized regression techniques (Lasso, Ridge and Elastic Net).