  Understanding how "black-box" models arrive at their predictions has sparked
significant interest from both within and outside the AI community. Our work
focuses on doing this by generating local explanations about individual
predictions for tree-based ensembles, specifically Gradient Boosting Decision
Trees (GBDTs). Given a correctly predicted instance in the training set, we
wish to generate a counterfactual explanation for this instance, that is, the
minimal perturbation of this instance such that the prediction flips to the
opposite class. Most existing methods for counterfactual explanations are (1)
model-agnostic, so they do not take into account the structure of the original
model, and/or (2) involve building a surrogate model on top of the original
model, which is not guaranteed to represent the original model accurately.
There exists a method specifically for random forests; we wish to extend this
method for GBDTs. This involves accounting for (1) the sequential dependency
between trees and (2) training on the negative gradients instead of the
original labels.
