When applied to binary data, most classification algorithms behave well provided the dataset is balanced. However, when one single class includes the majority of cases, a good predictive performance for the minority class is not easy to achieve. We examine the strengths and weaknesses of three tree-based models when dealing with imbalanced data.We also explore sampling and cost sensitive methods as strategies for improving machine learning algorithms. An application to a large dataset of breath alcohol content tests performed in Catalonia (Spain) to detect drunk drivers is shown. The Random Forest method proved to be the model of choice if a high performance is required, while down- sampling strategies resulted in a significant reduction in computing time. When predicting alcohol impairment, the area of control (built-up or not), hour of day and drivers age were the most relevant variables for classification.