小数据何时起作用？朝觐和副朝人群状态分类中表格基础模型与传统方法之间的准确性和效率权衡
When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah
AI总结：
研究在朝觐和副朝人群状态分类中，测试表格基础模型在标签稀缺时的作用，将其与传统机器学习方法比较，评估不同模型，揭示标签数量影响模型选择及两种方法在准确性和效率上的权衡。
摘要：
从少量带标签示例学习是表格机器学习核心挑战，在标签成本高的领域成约束。表格基础模型无需特定任务训练。本研究在人群状态分类中测试，对比标准机器学习方法，用三个真实数据集评估不同模型，结果表明选择取决于标签预算，总结出给定预算下的实用选择图。
英文摘要：
Learning from few labeled examples is a central challenge in tabular machine learning, and it becomes the binding constraint in domains where labeling is costly, such as crowd monitoring during Hajj and Umrah. Tabular foundation models, which predict from only a handful of examples without task-specific training, were recently introduced to address this very-few-label regime. In this study we test them on crowd-state classification to assess how much they help when labels are scarce, and we compare them against standard machine learning methods to characterize the accuracy and efficiency trade-offs between the two approaches. Using three real datasets we evaluate different machine learning models, in untuned and tuned forms, against three foundation models. Results show that no single family is best everywhere. The right choice depends on the label budget. When labels are very few, foundation models lead. As labels grow, tuned conventional models catch up and significantly surpass the foundation models on the more structural geometry target. Efficiency separates them further where tuned machine learning models incur a large tuning cost that foundation models avoid, although foundation models reprocess their context at every prediction. We summarize these results as a practical map of which approach to prefer under a given label budget and computational budget.
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