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：量化机器学习工作流中数据的价值，可在机器学习项目中发挥关键作用，助力制定更具战略性的决策。现有的基于Shapley值的机器学习数据估值框架计算成本高昂，因为需要通过大量重复模型训练来获取Shapley值。本文提出了一种高效的数据估值框架EcoVal，以快速且实用的方式估算机器学习模型的数据价值。我们并非直接处理单个数据样本，而是确定相似数据点集群的价值，并将该价值进一步传播至集群内所有成员点。研究表明，总体数据价值可通过估算每条数据的内在价值和外在价值来实现。这一方法基于将模型性能形式化为一种“生产函数”——该概念在传统自由经济市场中常用于根据劳动力和资本等因素估算产出量。我们为所提出的估值技术提供了形式化证明，并阐释了实现其加速性能的原理与机制。通过展示该方法在分布内数据和样本外数据上的有效性，我们证明了其在现实世界中的适用性。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。