Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy
Machine Learning
2020-12-09 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1911.04393,
title = {Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy},
author = {Michael Rapp and Eneldo Loza Mencía and Johannes Fürnkranz},
journal= {arXiv preprint arXiv:1911.04393},
year = {2020}
}