NBDT：神经支持决策树

• 2020 年 4 月 3 日
• 筆記

深度学习正被用于那些要求准确且合理预测的领域中，例如金融和医学成像。虽然最近的研究对模型预测做了因果解释，但很少有研究探究能直接解释的模型，以匹配最先进的精确度。在过去，决策树一直是平衡解释性和精确度的黄金标准。然而，最近人们试图将决策树和深度学习结合起来，得出了一些模型。这些模型（1）的精确度比现代神经网络要低得多（例如：残差网络ResNet），即使是在小数据集中（例如：MNIST）。而且，这些模型还（2）需要各种不同的系统构架，这使得操作人员不得不在精确性和解释性中做选择。

原文标题：NBDT: Neural-Backed Decision Trees

原文：Deep learning is being adopted in settings where accurate and justifiable predictions are required, ranging from finance to medical imaging. While there has been recent work providing post-hoc explanations for model predictions, there has been relatively little work exploring more directly interpretable models that can match state-of-the-art accuracy. Historically, decision trees have been the gold standard in balancing interpretability and accuracy. However, recent attempts to combine decision trees with deep learning have resulted in models that (1) achieve accuracies far lower than that of modern neural networks (e.g. ResNet) even on small datasets (e.g. MNIST), and (2) require significantly different architectures, forcing practitioners pick between accuracy and interpretability.

原文作者：Alvin Wan,Lisa Dunlap,Daniel Ho,Jihan Yin,Scott Lee,Henry Jin,Suzanne Petryk,Sarah Adel Bargal,Joseph E. Gonzalez

原文链接：https://arxiv.org/abs/2004.00221