Generating actionable interpretations from ensembles of decision trees
Machine-learned models are often perceived as "black boxes": they are given inputs and hopefully produce desired outputs. There are many circumstances, however, where human-interpretability is crucial to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter a prediction when the mutated instance is input back to the model.