Which Data Harms My Regression Model: Enhancing Model Performance on Low-Quality Data Through Fast Data Attribution

With the rapid advancement of model architectures, the accuracy of industrial predictive modeling now largely hinges on data quality. However, real-world industrial datasets frequently contain low-quality samples that compromise model performance. While existing data preprocessing methods can effectively remove salient outliers, they persistently struggle to detect latent anomalies. To address this challenge, this paper proposes a fast data attribution-based dataset selection method for regression models, termed ${\mathrm{F{\scriptscriptstyle AST}}DAR}$, which enables the model to identify training samples that are detrimental to its performance and subsequently perform dataset selection. ${\mathrm{F{\scriptscriptstyle AST}}DAR}$ integrates deep network data attribution into the Leave-One-Out (LOO) influence calculation paradigm of linear regression models through model linearization and parameter dimensionality reduction. Considering the synergy among samples, the truncated Monte Carlo method is adopted to estimate marginal influences of each sample, and sample utility is defined for dataset selection. Validation on real-world industrial datasets demonstrates the effectiveness and practicality of our method. Experimental results show that models trained on ${\mathrm{F{\scriptscriptstyle AST}}DAR}$-selected data achieve significant performance improvements on both validation and test sets, outperforming multiple baseline methods.