用简单模型理解复杂机器学习模型的运作机制
Understanding with toy surrogate models in machine learning
- 用规则列表、稀疏决策树等简化模型替代复杂模型
- 帮助非专家把握输入特征对输出的整体影响
- 适合希望解释黑箱模型的开发者与研究者
在自然科学与社会科学中，常使用极简且高度理想化的玩具模型来理解复杂现象。一些用于解释透明度低的机器学习（ML）模型的简化模型，如规则列表和稀疏决策树，与科学中的玩具模型具有相似性。这些模型使非专家能通过更简单的形式，全局理解一个复杂模型如何运作，突出输入空间中最相关的特征及其对输出的影响。其核心差异在于：科学玩具模型的目标是世界中的真实现象，而代理模型的目标是另一个模型本身。这一本质区别使得玩具代理模型（TSMs）成为理解理论的新研究对象，现有分析难以涵盖。本文提出一种通过此类简单模型全局理解复杂机器学习模型的理论框架。
原文摘要 · Abstract (English)
In the natural and social sciences, it is common to use toy models -- extremely simple and highly idealized representations -- to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and their effect on the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models.
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