Targeted synthetic data generation for tabular data via hardness characterization

Synthetic data generation has been proven successful in improving model performance and robustness in the context of scarce or low-quality data. Using the data valuation framework to statistically identify beneficial and detrimental observations, we introduce a novel augmentation pipeline that generates only highvalue training points based on hardness characterization. We first demonstrate via benchmarks on real data that Shapley-based data valuation methods perform comparably with learning-based methods in hardness characterisation tasks, while offering significant theoretical and computational advantages. Then, we show that synthetic data generators trained on the hardest points outperform non-targeted data augmentation on simulated data and on a large scale credit default prediction task. In particular, our approach improves the quality of out-of-sample predictions and it is computationally more efficient compared to non-targeted methods. Training complex machine learning models requires large amounts of data, but in real-world applications data may be of poor quality, insufficient in amount, or subject to privacy, safety, and regulatory limitations. Such challenges have sparked an interest in synthetic data generation (SDG), representing the practice of using available data to generate realistic synthetic samples (Lu et al., 2024). In this work, we argue that, when the objective is to use synthetic data to make an existing machine learning model better generalize to unseen data, augmenting only the hardest training points is more effective than augmenting the entire training dataset.