# Lassoed Forests: Random Forests with Adaptive Lasso Post-selection

arXiv: https://arxiv.org/abs/2511.06698 · HTML: https://mlsanity.com/paper/2511.06698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [Surprises in High-Dimensional Ridgeless Least Squares Interpolation](https://mlsanity.com/paper/1903.08560/) (2019)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Minimum-Norm Interpolation Under Covariate Shift](https://mlsanity.com/paper/2404.00522/) (2024)
- [Generalized Additive Model Selection](https://mlsanity.com/paper/1506.03850/) (2015)

## Abstract (arXiv)

Random forests are a statistical learning technique that use bootstrap aggregation to average high-variance and low-bias trees. Improvements to random forests, such as applying Lasso regression to the tree predictions, have been proposed in order to reduce model bias. However, these changes can sometimes degrade performance (e.g., an increase in mean squared error). In this paper, we show in theory that the relative performance of these two methods, standard and Lasso-weighted random forests, depends on the signal-to-noise ratio. We further propose a unified framework to combine random forests and Lasso selection by applying adaptive weighting and show mathematically that it can strictly outperform the other two methods. We compare the three methods through simulation, including bias-variance decomposition, error estimates evaluation, and variable importance analysis. We also show the versatility of our method by applications to a variety of real-world datasets.