A key challenge for decision makers when incorporating black box machine learned models into practice is being able to understand the predictions provided by these models. One proposed set of methods is training surrogate explainer models which approximate the more complex model. Explainer methods are generally classified as either local or global, depending on what portion of the data space they are purported to explain. The improved coverage of global explainers usually comes at the expense of explainer fidelity. One way of trading off the advantages of both approaches is to aggregate several local explainers into a single explainer model with improved coverage. However, the problem of aggregating these local explainers is computationally challenging, and existing methods only use heuristics to form these aggregations. In this paper we propose a local explainer aggregation method which selects local explainers using non-convex optimization. In contrast to other heuristic methods, we use an integer optimization framework to combine local explainers into a near-global aggregate explainer. Our framework allows a decision-maker to directly tradeoff coverage and fidelity of the resulting aggregation through the parameters of the optimization problem. We also propose a novel local explainer algorithm based on information filtering. We evaluate our algorithmic framework on two healthcare datasets---the Parkinson's Progression Marker Initiative (PPMI) data set and a geriatric mobility dataset---which is motivated by the anticipated need for explainable precision medicine. Our method outperforms existing local explainer aggregation methods in terms of both fidelity and coverage of classification and improves on fidelity over existing global explainer methods, particularly in multi-class settings where state-of-the-art methods achieve 70% and ours achieves 90%.

中文翻译：

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用于可解释预测的最优局部解释器聚合

在将黑盒机器学习模型纳入实践时，决策者面临的一个关键挑战是能够理解这些模型提供的预测。提出的一组方法是训练近似更复杂模型的代理解释器模型。解释器方法通常分为局部或全局，这取决于它们旨在解释数据空间的哪一部分。全局解释器覆盖率的提高通常是以牺牲解释器保真度为代价的。权衡这两种方法的优点的一种方法是将多个本地解释器聚合到一个具有改进覆盖范围的解释器模型中。然而，聚合这些本地解释器的问题在计算上具有挑战性，现有方法仅使用启发式方法来形成这些聚合。在本文中，我们提出了一种局部解释器聚合方法，该方法使用非凸优化来选择局部解释器。与其他启发式方法相比，我们使用整数优化框架将本地解释器组合成近乎全局的聚合解释器。我们的框架允许决策者通过优化问题的参数直接权衡结果聚合的覆盖率和保真度。我们还提出了一种基于信息过滤的新型本地解释器算法。我们在两个医疗保健数据集上评估我们的算法框架——帕金森病进展标记计划 (PPMI) 数据集和一个老年移动数据集——其动机是对可解释的精准医学的预期需求。