Preprint Open access
ReLaG: A Scalable Framework Generalizing Random Splits to Data with Latent Relations
Random splitting can yield non-independent train--test subsets when a dataset contains related samples, as is common in certain applications such as biochemical studies. This leads to overly optimistic generalization estimates. Here, we introduce ReLaG, a modality-agnostic framework that models sample relatedness throu …