Year: 2019
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Rapp, Michael, Mencía, Eneldo Loza, Fürnkranz, Johannes
Institution: Technische Universität Darmstadt
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2989001935