is possible to obtain feature importance scores, describing how important each feature is to the predictions made by the model (see [@lundberg2018consistent], for example). In addition, we use the feature importance scores in order to reduce the number of features prior to applying our proposed approach. The results reported in this manuscript were obtained by letting the agent select features out of the 50 most important features of a XGB model. We get similar results for the 100 most important features as well. The list of these 50 features appears in Appendix \[app:feat\].

Figure \[fig:performance\] shows the test AUC results of the proposed approach, comparing to DT and XGB. For every number of features $k$, the DT was developed up to depth $k$, the XGB model was trained on the subset of $k$ most important features of a full XGB model (trained on all features), and the RL agent was trained to choose $k-3$ features, as it was forced to select the age, gender and race features as starting point.

![Test AUC performance of DT, XGB and our proposed RL approach. The upper bound was obtained using all features; the same performance was achieved by both XGB and a MLP 