Don't Waste Your Time: Early Stopping Cross-Validation

arXiv:2405.03389v1 Announce Type: new
Abstract: State-of-the-art automated machine learning systems for tabular data often employ cross-validation; ensuring that measured performances generalize to unseen data, or that subsequent ensembling does not overfit. However, using k-fold cross-validation instead of holdout validation drastically increases the computational cost of validating a single configuration. While ensuring better generalization and, by extension, better performance, the additional cost is often prohibitive for effective model selection within a time budget. We aim to make model selection with cross-validation …

abstract art arxiv automated automated machine learning computational cost cs.ai cs.lg data however k-fold learning systems machine machine learning performances state systems tabular tabular data type validation waste while