Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy

Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz

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arXiv · 1911.04393

Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy

Michael Rapp·Eneldo Loza Mencía·Johannes Fürnkranz

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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.

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Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz. 2019-11-11. Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy. https://arxiv.org/abs/1911.04393

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