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 value of the data 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. The code is available at \underline{https://github.com/respai-lab/ecoval}.

翻译：量化机器学习工作流程中数据的价值，对于在机器学习项目中做出更具战略性的决策具有关键作用。现有的基于Shapley值的数据价值评估框架计算成本高昂，因为它们需要大量重复训练模型以获得Shapley值。本文提出一种高效的数据价值评估框架EcoVal，能够快速、实用地评估数据对机器学习模型的价值。该方法不直接处理单个数据样本，而是确定相似数据点集群的价值，并将该价值进一步传播至集群内所有成员点。我们证明，通过评估每个数据的内在价值与外在价值，即可确定数据的总体价值。这一评估机制的实现，是通过将模型性能表述为一种\textit{生产函数}——该概念在传统自由经济市场中常用于基于劳动力和资本等因素估算产出量。我们提供了该价值评估技术的正式证明，并阐明了其实现加速性能的原理与机制。通过展示该方法在分布内数据与样本外数据上的有效性，我们证明了其在实际应用中的可行性。本工作解决了机器学习模型中大规模高效数据价值评估的核心挑战之一。代码发布于 \underline{https://github.com/respai-lab/ecoval}。