Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley value based frameworks for data valuation in machine learning are computationally expensive as they require considerable amount of repeated training of the model to obtain the Shapley value. In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner. Instead of directly working with individual data sample, we determine the value of a cluster of similar data points. This value is further propagated amongst all the member cluster points. We show that the overall data value can be determined by estimating the intrinsic and extrinsic value of each data. This is enabled by formulating the performance of a model as a \textit{production function}, a concept which is popularly used to estimate the amount of output based on factors like labor and capital in a traditional free economic market. We provide a formal proof of our valuation technique and elucidate the principles and mechanisms that enable its accelerated performance. We demonstrate the real-world applicability of our method by showcasing its effectiveness for both in-distribution and out-of-sample data. This work addresses one of the core challenges of efficient data valuation at scale in machine learning models.

翻译：在机器学习工作流中量化数据的价值，对于制定更具战略性的机器学习决策具有关键作用。现有的基于Shapley值的机器学习数据估值框架计算开销极大，因为需要反复训练模型以获得Shapley值。本文提出了一种高效的数据估值框架EcoVal，能够快速、实用地估算机器学习模型中数据的价值。我们并非直接处理单个数据样本，而是确定相似数据点簇的价值，并将该价值进一步传播至簇内所有成员点。研究表明，通过估算每个数据的内在价值与外在价值可以确定整体数据价值。这一过程通过将模型性能表述为"生产函数"得以实现——该概念常用于传统自由经济市场中基于劳动力和资本等要素估算产出量。我们为估值技术提供了形式化证明，阐明其加速运行的原理与机制。通过展示该方法在分布内数据与样本外数据上的有效性，验证了其在真实场景中的适用性。本研究解决了机器学习模型中大规模高效数据估值这一核心挑战。