# A Projection Pursuit Forest Algorithm for Supervised Classification

arXiv: https://arxiv.org/abs/1807.07207 · HTML: https://mlsanity.com/paper/1807.07207/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Projection Pursuit Density Ratio Estimation](https://mlsanity.com/paper/2506.00866/) (2025)
- [Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections](https://mlsanity.com/paper/2205.05359/) (2022)
- [Biased Hypothesis Formation From Projection Pursuit](https://mlsanity.com/paper/2201.00889/) (2022)
- [An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Improvements](https://mlsanity.com/paper/2602.21130/) (2026)

## Abstract (arXiv)

This paper presents a new ensemble learning method for classification problems called projection pursuit random forest (PPF). PPF uses the PPtree algorithm introduced in Lee et al. (2013). In PPF, trees are constructed by splitting on linear combinations of randomly chosen variables. Projection pursuit is used to choose a projection of the variables that best separates the classes. Utilizing linear combinations of variables to separate classes takes the correlation between variables into account which allows PPF to outperform a traditional random forest when separations between groups occurs in combinations of variables. The method presented here can be used in multi-class problems and is implemented into an R (R Core Team, 2018) package, PPforest, which is available on CRAN, with development versions at https://github.com/natydasilva/PPforest.