In many supervised learning applications, understanding and visualizing the effects of the predictor variables on the predicted response is of paramount importance. A shortcoming of black box supervised learning models (e.g. complex trees, neural networks, boosted trees, random forests, nearest neighbours, local kernel‐weighted methods and support vector regression) in this regard is their lack of interpretability or transparency. Partial dependence plots, which are the most popular approach for visualizing the effects of the predictors with black box supervised learning models, can produce erroneous results if the predictors are strongly correlated, because they require extrapolation of the response at predictor values that are far outside the multivariate envelope of the training data. As an alternative to partial dependence plots, we present a new visualization approach that we term accumulated local effects plots, which do not require this unreliable extrapolation with correlated predictors. Moreover, accumulated local effects plots are far less computationally expensive than partial dependence plots. We also provide an R package ALEPlot as supplementary material to implement our proposed method.

中文翻译：

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在黑盒监督学习模型中可视化预测变量的影响

在许多监督学习应用中，理解和可视化预测变量对预测响应的影响至关重要。在这方面，黑匣子监督学习模型（例如，复杂树，神经网络，增强树，随机森林，最近的邻居，局部核加权方法和支持向量回归）的缺点是它们缺乏可解释性或透明性。部分依赖图是使用黑盒监督学习模型可视化预测器效果的最流行方法，如果预测器之间存在强相关性，则可能会产生错误的结果，因为它们需要外推预测器值之外的响应。训练数据的多元包络。作为偏倚图的替代方案，我们提出了一种新的可视化方法，我们称其为累积局部效应图，该方法不需要使用相关的预测变量进行这种不可靠的推断。而且，累积的局部效应图比部分依赖图要便宜得多。我们还提供了R包ALEPlot作为补充材料，以实现我们提出的方法。