# Combining Predictions under Uncertainty: The Case of Random Decision Trees

arXiv: https://arxiv.org/abs/2208.07403 · HTML: https://mlsanity.com/paper/2208.07403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Aleatoric and Epistemic Uncertainty with Random Forests](https://mlsanity.com/paper/2001.00893/) (2020)
- [Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest](https://mlsanity.com/paper/2103.16700/) (2021)
- [Aggregation of Classifiers: A Justifiable Information Granularity Approach](https://mlsanity.com/paper/1703.05411/) (2017)
- [Combining Multiple Algorithms in Classifier Ensembles using Generalized Mixture Functions](https://mlsanity.com/paper/1806.01540/) (2018)

## Abstract (arXiv)

A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class. The latter takes uncertainty into account, but not the reliability of the uncertainty estimates (so to say, the "uncertainty about the uncertainty"). More generally, much remains unknown about how to best combine probabilistic estimates from multiple sources. In this paper, we investigate a number of alternative prediction methods. Our methods are inspired by the theories of probability, belief functions and reliable classification, as well as a principle that we call evidence accumulation. Our experiments on a variety of data sets are based on random decision trees which guarantees a high diversity in the predictions to be combined. Somewhat unexpectedly, we found that taking the average over the probabilities is actually hard to beat. However, evidence accumulation showed consistently better results on all but very small leafs.