决策树补偿模型误指定
Decision trees compensate for model misspecification
摘要：
ML中表现最好的模型是不可解释的。如果我们能够解释为什么它们表现出色，我们就能够复制这些机制，并获得可解释性和性能。一个例子是决策树及其下降梯度提升机（GBM）。这些在存在复杂交互时表现良好，树深度控制交互顺序。然而，相互作用不能完全解释在实践中发现的树的深度。我们确认了5个替代假设树的深度在没有真正的相互作用的情况下，性能的作用，并提出了一组数据集上的实验结果。树模型的成功部分是由于它们对各种形式的错误规范的鲁棒性。我们提出了两种方法，强大的广义线性模型（GLM）解决复合和混合响应的情况。
英文摘要：
The best-performing models in ML are not interpretable. If we can explain why they outperform, we may be able to replicate these mechanisms and obtain both interpretability and performance. One example are decision trees and their descendent gradient boosting machines (GBMs). These perform well in the presence of complex interactions, with tree depth governing the order of interactions. However, interactions cannot fully account for the depth of trees found in practice. We confirm 5 alternative hypotheses about the role of tree depth in performance in the absence of true interactions, and present results from experiments on a battery of datasets. Part of the success of tree models is due to their robustness to various forms of mis-specification. We present two methods for robust generalized linear models (GLMs) addressing the composite and mixed response scenarios.
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。
