My new Publication in @EPA_Journal is #openSource . Abstract The article introduces machine learning algorithms for political scientists. These approaches should not be seen as new method for old problems. Rather, it is important to understand the different logic of the machine learning approach. Here, data is analyzed without theoretical assumptions about possible causalities. Models are optimized according to their accuracy and robustness. While the computer can do this work more or less alone, it is the researcher’s duty to make sense of these models afterwards. Visualization of machine learning results therefore becomes very important and is in the focus of this paper. The methods that are presented and compared are decision trees, bagging and random forests. The later are more advanced versions of the former, relying on bootstrapping procedures. To demonstrate these methods, extreme shifts in the US budget and their connection to attention of political actors are analyzed. Th