# Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests

arXiv: https://arxiv.org/abs/2408.07151 · HTML: https://mlsanity.com/paper/2408.07151/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest](https://mlsanity.com/paper/2103.16700/) (2021)
- [ForestPrune: Compact Depth-Controlled Tree Ensembles](https://mlsanity.com/paper/2206.00128/) (2022)

## Built on by (most cited first, 2 of 2)

- [Targeted tuning of random forests for quantile estimation and prediction intervals](https://mlsanity.com/paper/2507.01430/) (2025)
- [Optimized Sequential Testing for Binary Ensemble Classifiers](https://mlsanity.com/paper/2606.15237/) (2026)

## Abstract (arXiv)

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.