关于随机森林行为的符号编码
On Symbolically Encoding the Behavior of Random Forests
摘要：
最近的工作表明，一些机器学习系统的输入-输出行为可以使用布尔表达式或易处理的布尔电路来符号化地捕获，这有助于对这些系统的行为进行推理。虽然大部分的重点一直在系统的布尔输入和输出，我们解决系统的离散输入和输出，包括离散连续变量的系统中的基础上决策树。我们还专注于适合的编码计算素蕴涵，最近发挥了核心作用，解释机器学习系统的决定。我们展示了可满足性编码的一些关键区别，并提出了一种针对给定任务的完整编码。
英文摘要：
Recent work has shown that the input-output behavior of some machine learning systems can be captured symbolically using Boolean expressions or tractable Boolean circuits, which facilitates reasoning about the behavior of these systems. While most of the focus has been on systems with Boolean inputs and outputs, we address systems with discrete inputs and outputs, including ones with discretized continuous variables as in systems based on decision trees. We also focus on the suitability of encodings for computing prime implicants, which have recently played a central role in explaining the decisions of machine learning systems. We show some key distinctions with encodings for satisfiability, and propose an encoding that is sound and complete for the given task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
