the action space corresponds to the set of available input features. At each time step through an episode, a RL agent chooses a feature whose value is masked or unknown, and gets to observe the value of the specific feature. Once confident, the agent may choose to predict the label of the input sample and is rewarded based on the quality of the prediction. Throughout training, the agent learns to “ask” for the features which are most helpful for an accurate prediction of the label. The set of selected features may differ for each input sample, and the features are selected gradually and in adaptive fashion, so that each feature is selected based on the previously selected ones and their corresponding values. In this sense, the agent behaves in a more human-like fashion, comparing to standard feature selection models. Moreover, the trajectory (of selected features and their corresponding values) leading to each prediction is fully transparent and hence contributes to the model’s interpretability.

We apply our approach to a national survey dataset, containing answers of patients to a large set of questions. We demonstrate that when limiting the prediction to be based on a small number of features, our approach 