Print Email Facebook Twitter Robustness of optimal randomized decision trees with dynamic programming Title Robustness of optimal randomized decision trees with dynamic programming Author Götz, Valentijn (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor van der Linden, J.G.M. (mentor) Demirović, E. (mentor) Oliehoek, F.A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-02-03 Abstract Decision tree learning is widely done heuristically, but advances in the field of optimal decision trees have made them a more prominent subject of research. However, current methods for optimal decision trees tend to overlook the metric of robustness. Our research wants to find out whether the robustness of optimal decision trees can be improved by incorporating randomization. To achieve this, we added randomization to the existing MurTree algorithm, and performed experiments to compare the robustness. The results show that adding randomization improves the robustness of the decision tree but lowers the out of sample accuracy. To reference this document use: http://resolver.tudelft.nl/uuid:13b8e243-c192-4e7b-82fe-e7ce90aab671 Part of collection Student theses Document type bachelor thesis Rights © 2023 Valentijn Götz Files PDF RPProject_6_.pdf 532.95 KB Close viewer /islandora/object/uuid:13b8e243-c192-4e7b-82fe-e7ce90aab671/datastream/OBJ/view