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.

翻译：在现实世界中使用机器学习系统常常存在问题，例如难以解释的黑箱模型、对不完美测量的假设确定性，或仅提供单一分类而非概率分布。本文引入了不决树（Indecision Trees），这是对决策树（Decision Trees）的一种改进，能够在不确定性下学习、在不确定性下进行推理、提供关于可能标签的稳健分布，并可拆解为一组逻辑论据，供其他推理系统使用。