we choose to compare our result to two off-the-shelf classifiers: a Decision Tree (DT) and XGBoost (XGB) [@chen2016xgboost].

A decision tree is a fundamental and widely-used machine learning algorithm. It has several known disadvantages, such as its greedy training procedure and its simplistic modeling of the feature space (axis-aligned rectangles), which often prevent it from performing on par with the state-of-the-art models. However, in many cases it is nevertheless a strong performer. In our context, a DT has three attractive attributes which make it an appropriate baseline: first, specifying the depth of the tree, we can limit the number of features leading to each prediction made by the model. Second, different patients might correspond to different paths down the tree, so that different subsets of features may be used to obtain the predictions of different patients. Third, a DT is fully transparent, so that the predictions have high interpretability.

XGBoost is arguably considered as the state-of-the-art model for tabular data and is a popular choice by practitioners. Being an ensemble method, its interpretability is low, in the sense that it is difficult to provide a clear reasoning to the prediction made by the algorithm. Yet, given a trained model it 