  Gradient boosted models are a fundamental machine learning technique.
Robustness to small perturbations of the input is an important quality measure
for machine learning models, but the literature lacks a method to prove the
robustness of gradient boosted models. This work introduces VeriGB, a tool for
quantifying the robustness of gradient boosted models. VeriGB encodes the model
and the robustness property as an SMT formula, which enables state of the art
verification tools to prove the model's robustness. We extensively evaluate
VeriGB on publicly available datasets and demonstrate a capability for
verifying large models. Finally, we show that some model configurations tend to
be inherently more robust than others.
