Print Email Facebook Twitter Robust OCTs Title Robust OCTs: Investigating classification tree robustness Author Lek, Gert (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Postek, K.S. (mentor) Degree granting institution Delft University of Technology Programme Applied Mathematics Date 2022-06-20 Abstract 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. Subject Decision treerobustoptimisationmachine learninginterpretableROCT To reference this document use: http://resolver.tudelft.nl/uuid:c3a9e0c7-0f44-4213-b714-b337ba162517 Part of collection Student theses Document type bachelor thesis Rights © 2022 Gert Lek Files PDF Bachelor_Thesis_Gert_Lek_19_.pdf 411.93 KB Close viewer /islandora/object/uuid:c3a9e0c7-0f44-4213-b714-b337ba162517/datastream/OBJ/view