ID  - mr2019barxiv
T1  - Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy
A1  - Rapp, Michael
A1  - Loza Mencía, Eneldo
A1  - Fürnkranz, Johannes
Y1  - 2019
M1  - ArXiv e-prints
IS  - 1911.04393
T2  - Knowledge Engineering Group, Technische Universität Darmstadt
UR  - https://arxiv.org/abs/1911.04393
N2  - We analyze the trade-off between model complexity and accuracy for random forests by breaking the trees up into individual classification rules and selecting a subset of them. We show experimentally that already a few rules are sufficient to achieve an acceptable accuracy close to that of the original model. Moreover, our results indicate that in many cases, this can lead to simpler models that clearly outperform the original ones.
M1  - archiveprefix={arXiv}
M1  -  eprint={1911.04393}
M1  -  primaryclass={cs.LG}
ER  -