Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning

• URL: http://arxiv.org/abs/2508.16255v1
• Date: Fri, 22 Aug 2025 09:40:58 GMT
• Title: Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning
• Authors: Andreas Loizou, Dimitrios Tsoumakos,
• Abstract summary: We present a Data Shapley approach to identify a dataset's high-quality data computations, Chunked Data Shapley (C-DaSh)<n>C-DaSh scalably divides dataset into manageable chunks and estimates contribution of each chunk using optimized selection and gradient descent.<n>We empirically benchmark our method on diverse real-world classification and regression tasks, demonstrating that C-DaSh outperforms existing Shapley approximations in both computational efficiency (achieving speedups between 80x - 2300x) and accuracy in detecting low-quality data regions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As the volume and diversity of available datasets continue to increase, assessing data quality has become crucial for reliable and efficient Machine Learning analytics. A modern, game-theoretic approach for evaluating data quality is the notion of Data Shapley which quantifies the value of individual data points within a dataset. State-of-the-art methods to scale the NP-hard Shapley computation also face severe challenges when applied to large-scale datasets, limiting their practical use. In this work, we present a Data Shapley approach to identify a dataset's high-quality data tuples, Chunked Data Shapley (C-DaSh). C-DaSh scalably divides the dataset into manageable chunks and estimates the contribution of each chunk using optimized subset selection and single-iteration stochastic gradient descent. This approach drastically reduces computation time while preserving high quality results. We empirically benchmark our method on diverse real-world classification and regression tasks, demonstrating that C-DaSh outperforms existing Shapley approximations in both computational efficiency (achieving speedups between 80x - 2300x) and accuracy in detecting low-quality data regions. Our method enables practical measurement of dataset quality on large tabular datasets, supporting both classification and regression pipelines.

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