# Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles

arXiv: https://arxiv.org/abs/2102.00415 · HTML: https://mlsanity.com/paper/2102.00415/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2021.

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

- [Splitting criteria for ordinal decision trees: an experimental study](https://mlsanity.com/paper/2412.13697/) (2024)
- [Sequential Permutation Testing of Random Forest Variable Importance Measures](https://mlsanity.com/paper/2206.01284/) (2022)
- [Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment](https://mlsanity.com/paper/2403.06829/) (2024)
- [Ordinal Mixed-Effects Random Forest](https://mlsanity.com/paper/2406.03130/) (2024)

## Abstract (arXiv)

Existing ordinal trees and random forests typically use scores that are assigned to the ordered categories, which implies that a higher scale level is used. Versions of ordinal trees are proposed that take the scale level seriously and avoid the assignment of artificial scores. The basic construction principle is based on an investigation of the binary models that are implicitly used in parametric ordinal regression. These building blocks can be fitted by trees and combined in a similar way as in parametric models. The obtained trees use the ordinal scale level only. Since binary trees and random forests are constituent elements of the trees one can exploit the wide range of binary trees that have already been developed. A further topic is the potentially poor performance of random forests, which seems to have ignored in the literature. Ensembles that include parametric models are proposed to obtain prediction methods that tend to perform well in a wide range of settings. The performance of the methods is evaluated empirically by using several data sets.