【UC伯克利】可解释性机器学习:定义、方法和应用

1 月 19 日 专知

【导读】可解释性是机器学习中一个快速发展的领域,并且有许多工作研究解释的各个方面。一些文章综述了对深度学习模型的事后解释的不同方法,并指出他们之间的相似之处。另外一些工作则是聚焦于如何评估解释。这些方法虽然从不同的角度出发,但是没有把解决可解释性机器学习的问题看成一个整体,并且也没有说明如何将它应用到数据科学的生命周期中。这篇文章详细解释了可解释性机器学习的定义,方法和应用。


【摘要】机器学习模型在学习复杂模式方面取得了巨大成功,它们能够对未观察到的数据进行预测。除了使用模型进行预测外,解释模型所学知识的能力正在受到越来越多的关注。然而,这种日益增加的焦点导致了对可解释性概念的相当大的混淆。特别是,尚不清楚提出的各种解释方法之间的关联关系,以及可以用什么来评估它们。

我们的目标是通过在机器学习的背景下定义可解释性来解决这些问题并引入预测,描述,相关(PDR)框架来讨论可解释性。PDR框架为评估提供了三个总体需求:预测准确性,描述准确性和相关性,相对于人类受众判断相关性。此外,我们将可解释性现有技术分为基于模型和后期,包括稀疏性,模块性和可模拟性。为了展示如何使用PDR框架来评估和解释,我们提供了许多真实的例子。=最后,基于我们的框架,我们讨论了现有方法的局限性未来工作的方向。

论文地址:

http://www.zhuanzhi.ai/paper/73927a7e5a40822c798c9d605cb81cd4

https://arxiv.org/abs/1901.04592

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