{"paper":{"title":"Efficient, adaptive cross-validation for tuning and comparing models, with application to drug discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Hui Shen, Jacqueline M. Hughes-Oliver, William J. Welch","submitted_at":"2012-02-29T13:12:51Z","abstract_excerpt":"Cross-validation (CV) is widely used for tuning a model with respect to user-selected parameters and for selecting a \"best\" model. For example, the method of $k$-nearest neighbors requires the user to choose $k$, the number of neighbors, and a neural network has several tuning parameters controlling the network complexity. Once such parameters are optimized for a particular data set, the next step is often to compare the various optimized models and choose the method with the best predictive performance. Both tuning and model selection boil down to comparing models, either across different val"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1202.6536","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}