# Sparse Projection Oblique Randomer Forests

arXiv: https://arxiv.org/abs/1506.03410 · HTML: https://mlsanity.com/paper/1506.03410/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2015.

## Built on by (most cited first, 15 of 15)

- [Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://mlsanity.com/paper/2212.02934/) (2022)
- [OPORP: One Permutation + One Random Projection](https://mlsanity.com/paper/2302.03505/) (2023)
- [Stabilizing Estimates of Shapley Values with Control Variates](https://mlsanity.com/paper/2310.07672/) (2023)
- [When are Deep Networks really better than Decision Forests at small sample sizes, and how?](https://mlsanity.com/paper/2108.13637/) (2021)
- [Differential Privacy with Random Projections and Sign Random Projections](https://mlsanity.com/paper/2306.01751/) (2023)
- [Minimax Rates for High-Dimensional Random Tessellation Forests](https://mlsanity.com/paper/2109.10541/) (2021)
- [Accelerated and interpretable oblique random survival forests](https://mlsanity.com/paper/2208.01129/) (2022)
- [Statistical Advantages of Oblique Randomized Decision Trees and Forests](https://mlsanity.com/paper/2407.02458/) (2024)
- [Dive into Decision Trees and Forests: A Theoretical Demonstration](https://mlsanity.com/paper/2101.08656/) (2021)
- [Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web](https://mlsanity.com/paper/2408.14636/) (2024)
- [Oblique Bayesian additive regression trees](https://mlsanity.com/paper/2411.08849/) (2024)
- [Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation](https://mlsanity.com/paper/2502.00465/) (2025)
- [Decision Tree Embedding by Leaf-Means](https://mlsanity.com/paper/2512.01819/) (2025)
- [Jacobian Aligned Random Forests](https://mlsanity.com/paper/2512.08306/) (2025)
- [Vectorized Dynamic Histograms for Sparse Oblique Forests](https://mlsanity.com/paper/2603.00326/) (2026)

## Abstract (arXiv)

Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many recent extensions to decision forests are based on axis-oblique splits. Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency. We introduce yet another decision forest, called "Sparse Projection Oblique Randomer Forests" (SPORF). SPORF uses very sparse random projections, i.e., linear combinations of a small subset of features. SPORF significantly improves accuracy over existing state-of-the-art algorithms on a standard benchmark suite for classification with >100 problems of varying dimension, sample size, and number of classes. To illustrate how SPORF addresses the limitations of both axis-aligned and existing oblique decision forest methods, we conduct extensive simulated experiments. SPORF typically yields improved performance over existing decision forests, while mitigating computational efficiency and scalability and maintaining interpretability. SPORF can easily be incorporated into other ensemble methods such as boosting to obtain potentially similar gains.