黑盒之外的学习：对可解释模型的追求
Learning outside the Black-Box: The pursuit of interpretable models
摘要：
机器学习已经证明了它能够产生准确的模型，但是在机器学习社区之外部署这些模型受到解释这些模型的困难的阻碍。本文提出了一种算法，产生一个连续的全球任何给定的连续黑盒函数的解释。我们的算法采用投影寻踪的变化，其中脊函数被选择为Meijer G-函数，而不是通常的多项式样条。由于Meijer G-函数的参数是可微的，我们可以通过梯度下降来调整表示的参数;因此，我们的算法是有效的。使用五个熟悉的数据集从UCI存储库和两个熟悉的机器学习算法，我们证明了我们的算法产生的全球性的解释，都是高度准确和简约（涉及少量的条款）。我们的解释允许容易理解的功能和功能的相互作用的相对重要性。我们的解释算法代表了一个飞跃，从以前的国家的艺术。
英文摘要：
Machine Learning has proved its ability to produce accurate models but the deployment of these models outside the machine learning community has been hindered by the difficulties of interpreting these models. This paper proposes an algorithm that produces a continuous global interpretation of any given continuous black-box function. Our algorithm employs a variation of projection pursuit in which the ridge functions are chosen to be Meijer G-functions, rather than the usual polynomial splines. Because Meijer G-functions are differentiable in their parameters, we can tune the parameters of the representation by gradient descent; as a consequence, our algorithm is efficient. Using five familiar data sets from the UCI repository and two familiar machine learning algorithms, we demonstrate that our algorithm produces global interpretations that are both highly accurate and parsimonious (involve a small number of terms). Our interpretations permit easy understanding of the relative importance of features and feature interactions. Our interpretation algorithm represents a leap forward from the previous state of the art.
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。
