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,使用机器学习系统往往会有问题,因为无法解释的黑盒模型、假定不完善测量的确定性,或者提供单一的分类而不是概率分布。 本文介绍了《决策树 》 ( Indecious Trees), “ 决策树”的修改在不确定性下学习,可以在不确定性下进行推断,为可能的标签提供强有力的分布,并可以拆编成一套逻辑参数,供其他推理系统使用。