Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests

Nikola Surjanovic; Andrew Henrey; Thomas M. Loughin

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arXiv · 2408.07151

Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests

Nikola Surjanovic·Andrew Henrey·Thomas M. Loughin

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Abstract

We demonstrate that adaptively controlling the size of individual regression trees in a random forest can improve predictive performance, contrary to the conventional wisdom that trees should be fully grown. A fast pruning algorithm, alpha-trimming, is proposed as an effective approach to pruning trees within a random forest, where more aggressive pruning is performed in regions with a low signal-to-noise ratio. The amount of overall pruning is controlled by adjusting the weight on an information criterion penalty as a tuning parameter, with the standard random forest being a special case of our alpha-trimmed random forest. A remarkable feature of alpha-trimming is that its tuning parameter can be adjusted without refitting the trees in the random forest once the trees have been fully grown once. In a benchmark suite of 46 example data sets, mean squared prediction error is often substantially lowered by using our pruning algorithm and is never substantially increased compared to a random forest with fully-grown trees at default parameter settings.

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Nikola Surjanovic, Andrew Henrey, Thomas M. Loughin. 2024-08-13. Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests. https://arxiv.org/abs/2408.07151

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