Robustness of optimal randomized decision trees with dynamic programming

Bachelor thesis (2023)

Authors

V.J. Götz Electrical Engineering, Mathematics and Computer Science

Contributors

J.G.M. van der Linden Algorithmics - EEMCS (supervisor 1)
E. Demirović Algorithmics - EEMCS (supervisor 1)
F.A. Oliehoek Interactive Intelligence - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Valentijn Götz
More Info
expand_more

Published Date

03-02-2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

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.

Files

RPProject_6_.pdf
(.pdf | 0.52 Mb)