network.[]{data-label="fig:performance"}](performances3.png){width="100.00000%"}

Our proposed RL approach consistently outperforms both the DT and the XGB models for all tested numbers of features. Moreover, the RL models improve monotonically as more features are allowed to be selected, as is also the case for the XGB models. The DT models, however, start to overfit for more than 6 features.

The advantage of the proposed approach over XGB manifests not only in terms of prediction accuracy, but also in terms of interpretability, through fact that one gets to observe the sequence of unmasked features leading to the each prediction. the predictions of our approach are provided in Appendix \[app:examples\].

Figure \[fig:cases\] shows two case studies of the model predictions for patients from the test set.

![Two case studies of the agent behavior.[]{data-label="fig:cases"}](case_1.png "fig:"){width="49.00000%"} ![Two case studies of the agent behavior.[]{data-label="fig:cases"}](case_2.png "fig:"){width="49.00000%"}

On the left hand side of Figure \[fig:cases\] the agent acts on the data of two patients from the same race, having similar age and different sex. For both patients the agent chooses to unmask a feature containing information about the patients’ liver condition (marked in red). The bottom patient had a liver condition while the top patient did not have such condition. From 