Several sophisticated machine learning tools (e.g., ensembles or deep networks) have shown outstanding performance in different regression forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Nevertheless, decision makers frequently require more than a black box model to be able to “trust” the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning research. This paper proposes a series of visualization tools that explain the relationship between the expected predictive performance of black box regression models and the values of the input variables of any given test case. This type of information thus allows end‐users to correctly assess the risks associated with the use of a model, by showing how concrete values of the predictors may affect the performance of the model. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end‐user. Furthermore, a thorough evaluation of the proposed tools is performed to showcase the reliability of this approach.

中文翻译：

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回归误差的可视化解释

几种复杂的机器学习工具（例如合奏或深度网络）在不同的回归预测任务中表现出出色的性能。在许多实际应用领域中，模型的数字预测会推动重要而昂贵的决策。然而，决策者经常需要的不仅仅是黑匣子模型，以使其能够“信任”这些预测，直到他们将决策基于其基础上。在这种情况下，了解这些黑匣子已成为机器学习研究中的热门话题之一。本文提出了一系列可视化工具，它们解释了黑盒回归模型的预期预测性能与任何给定测试用例的输入变量的值之间的关系。因此，此类信息可通过显示预测变量的具体值如何影响模型的性能，使最终用户正确评估与使用模型相关的风险。我们的插图包含不同的现实世界数据集和学习算法，可深入了解这些工具带给数据分析师和最终用户的使用类型和信息。此外，对提出的工具进行了彻底的评估，以展示这种方法的可靠性。