# Cost-complexity pruning of random forests

arXiv: https://arxiv.org/abs/1703.05430 · HTML: https://mlsanity.com/paper/1703.05430/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2017.

## Built on by (most cited first, 1 of 1)

- [Tree in Tree: from Decision Trees to Decision Graphs](https://mlsanity.com/paper/2110.00392/) (2021)

## Abstract (arXiv)

Random forests perform bootstrap-aggregation by sampling the training samples with replacement. This enables the evaluation of out-of-bag error which serves as a internal cross-validation mechanism. Our motivation lies in using the unsampled training samples to improve each decision tree in the ensemble. We study the effect of using the out-of-bag samples to improve the generalization error first of the decision trees and second the random forest by post-pruning. A preliminary empirical study on four UCI repository datasets show consistent decrease in the size of the forests without considerable loss in accuracy.