论文标题
破解黑匣子：蒸馏深度运动分析
Cracking the Black Box: Distilling Deep Sports Analytics
论文作者
论文摘要
本文解决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。神经网通过深度学习实现了极高的预测准确性，并且在体育分析中很受欢迎。但是，很难解释神经网络模型，并且更难从中隐含的知识中提取可行的见解。因此，我们构建了一个简单透明的模型，该模型模仿了原始深度学习模型的输出，并以明确的可解释方式代表了学习的知识。我们的模拟模型是一个线性模型树，将线性模型的集合与回归树结构结合在一起。神经网络的树版本可实现高忠诚，解释自己，并为运动员和教练等专业利益相关者提供见解。我们建议并比较几种可扩展的模型树学习启发式方法，以解决数百万个数据点的数据集的计算挑战。
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.
