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 (mentor)
E. Demirović Algorithmics - EEMCS (mentor)
F.A. Oliehoek Interactive Intelligence - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Valentijn Götz
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Published Date

03-02-2023

Language

English

Reuse Rights

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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)
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