破解黑匣子：提取深度体育分析
Cracking the Black Box: Distilling Deep Sports Analytics
摘要：
本文讨论了应用于体育分析的深度学习的准确性和透明度之间的权衡。神经网络通过深度学习实现了很高的预测准确性，在体育分析中很受欢迎。但是，解释神经网络模型很难，更难从其中隐含的知识中提取可操作的见解。因此，我们构建了一个简单透明的模型，模仿原始深度学习模型的输出，并以明确的可解释方式表示学习的知识。我们的模拟模型是一个线性模型树，它结合了线性模型的集合与回归树结构。树型神经网络实现了高保真度，解释了自己，并为运动员和教练等专家利益相关者提供了见解。我们提出并比较了几种可扩展的模型树学习算法，以解决具有数百万数据点的数据集的计算挑战。
英文摘要：
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics. Neural nets achieve great predictive accuracy through deep learning, and are popular in sports analytics. But it is hard to interpret a neural net model and harder still to extract actionable insights from the knowledge implicit in it. Therefore, we built a simple and transparent model that mimics the output of the original deep learning model and represents the learned knowledge in an explicit interpretable way. Our mimic model is a linear model tree, which combines a collection of linear models with a regression-tree structure. The tree version of a neural network achieves high fidelity, explains itself, and produces insights for expert stakeholders such as athletes and coaches. We propose and compare several scalable model tree learning heuristics to address the computational challenge from datasets with millions of data points.
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。
