The efforts to understand the model's performance could also be extended to try and reverse engineer the most impactful features and build a fully human-interpretable model. Building a single decision tree, ideally at minimal loss of performance due to the reduction of model complexity, with the most impactful features could start to approximate the more complex GBT model. This would make patterns detected by the model more accessible, as well as generally improve the trust in the model.