# Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty

arXiv: https://arxiv.org/abs/2206.12252 · HTML: https://mlsanity.com/paper/2206.12252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [Ternary Decision Trees with Locally-Adaptive Uncertainty Zones](https://mlsanity.com/paper/2605.22740/) (2026)

## Abstract (arXiv)

Using Machine Learning systems in the real world can often be problematic, with inexplicable black-box models, the assumed certainty of imperfect measurements, or providing a single classification instead of a probability distribution. This paper introduces Indecision Trees, a modification to Decision Trees which learn under uncertainty, can perform inference under uncertainty, provide a robust distribution over the possible labels, and can be disassembled into a set of logical arguments for use in other reasoning systems.