While the approach is presented as a general generative model based on DT and RF, the paper fails to show its practical interest beyond handling missing values at test time. The possibility of using the approach for outlier detection is potentially interesting but the experiment in the paper is restricted to a single dataset and does not include any comparison with competitors except Gaussian KDE. Overall, the properties of GeDT and GeRF as general purpose density estimators are not really studied. My feeling is that because the tree partitioning is unchanged with respect to standard discriminative DT and RF, GeDT and GeRF are probably only appropriate in the context of tasks related to target predictions. In other tasks, I don't see why they would perform better than pure PC models or other methods mentioned in the related work section.