Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation
Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation
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DOI：
10.1080/10618600.2014.907095
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发表时间：
2015-01-02
影响因子：
2.4
通讯作者：
Pitkin, Emil
中科院分区：
文献类型：
--
作者：
Goldstein, Alex;Kapelner, Adam;Pitkin, Emil
本文介绍了个体条件期望（ICE）图，这是一种将任何监督学习算法估计的模型可视化的工具。经典的偏相关图（pdp）有助于可视化预测响应与一个或多个特征之间的平均偏关系。在存在大量相互作用效应的情况下，部分响应关系可能是异质的。因此，平均曲线（如PDP）可能会混淆建模关系的复杂性。因此，ICE图通过绘制预测响应与单个观测值特征之间的函数关系来改进PDP。具体来说，ICE图突出了协变量范围内拟合值的变化，表明异质性可能存在的位置和程度。除了提供用于探索性分析的绘图套件外，我们还在数据生成模型中包含了对附加结构的视觉测试。通过模拟示例和真实数据集，我们展示了ICE图如何能够以pdp无法做到的方式揭示估计模型。程序概述可在R包ICEbox。
This article presents individual conditional expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. Classical partial dependence plots (PDPs) help visualize the average partial relationship between the predicted response and one or more features. In the presence of substantial interaction effects, the partial response relationship can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate the complexity of the modeled relationship. Accordingly, ICE plots refine the PDP by graphing the functional relationship between the predicted response and the feature for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate, suggesting where and to what extent heterogeneities might exist. In addition to providing a plotting suite for exploratory analysis, we include a visual test for additive structure in the data-generating model. Through simulated examples and real datasets, we demonstrate how ICE plots can shed light on estimated models in ways PDPs cannot. Procedures outlined are available in the R package ICEbox.