The application of machine learning in daily life requires interpretability and robustness. In this paper we try to make the process of building robust and interpretable decision trees more accessible. We do this by making the fitting of these models cheaper and simpler. We build on previous research and see if changing input data or the fitting formulation can create more robust trees that can be computed faster. To investigate this, we test whether data perturbations make heuristic algorithms more robust and whether enforcing constraints on adversarial examples in normal optimal classifica- tion tree MILP formulations can improve robustness. We also provide an altered formulation for the robust OCT model in Vos and Verwer (2021b) that yields better results with shorter runtimes. Finally, we extend the ROCT formulation to be applicable to multi-class classification and regression tasks.