One major cause of performance degradation in predictive models is that the test samples are not well covered by the training data. Such not well-represented samples are called OoD samples. In this article, we propose OoDAnalyzer, a visual analysis approach for interactively identifying OoD samples and explaining them in context. Our approach integrates an ensemble OoD detection method and a grid-based visualization. The detection method is improved from deep ensembles by combining more features with algorithms in the same family. To better analyze and understand the OoD samples in context, we have developed a novel $k$ NN-based grid layout algorithm motivated by Hall's theorem. The algorithm approximates the optimal layout and has $O(kN^2)$ time complexity, faster than the grid layout algorithm with overall best performance but $O(N^3)$ time complexity. Quantitative evaluation and case studies were performed on several datasets to demonstrate the effectiveness and usefulness of OoDAnalyzer.

中文翻译：

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OoDAnalyzer：超分布样本的交互式分析

预测模型性能下降的一个主要原因是训练数据没有很好地覆盖测试样本。这种表现不佳的样本称为 OoD 样本。在本文中，我们提出了 OoDAnalyzer，这是一种可视化分析方法，用于交互式识别 OoD 样本并在上下文中对其进行解释。我们的方法集成了集成 OoD 检测方法和基于网格的可视化。通过将更多特征与同一族中的算法相结合，从深度集成改进了检测方法。为了更好地分析和理解上下文中的 OoD 样本，我们开发了一种新颖的$千$ 受霍尔定理启发的基于 NN 的网格布局算法。该算法近似最优布局，并具有$O(kN^2)$ 时间复杂度，比具有最佳性能的网格布局算法快，但 $O(N^3)$时间复杂度。对多个数据集进行了定量评估和案例研究，以证明 OoDAnalyzer 的有效性和实用性。