{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-and-informative-model-selection-using","title":"Fast and Informative Model Selection using Learning Curve Cross-Validation","arxiv_id":"2111.13914","date":"2021-11-27","proceeding":null,"authors":["Felix Mohr","Jan N. van Rijn"],"abstract":"Common cross-validation (CV) methods like k-fold cross-validation or Monte-Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing on the remaining data. These techniques have two major drawbacks. First, they can be unnecessarily slow on large datasets. Second, beyond an estimation of the final performance, they give almost no insights into the learning process of the validated algorithm. In this paper, we present a new approach for validation based on learning curves (LCCV). Instead of creating train-test splits with a large portion of training data, LCCV iteratively increases the number of instances used for training. In the context of model selection, it discards models that are very unlikely to become competitive. We run a large scale experiment on the 67 datasets from the AutoML benchmark and empirically show that in over 90% of the cases using LCCV leads to similar performance (at most 1.5% difference) as using 5/10-fold CV. However, it yields substantial runtime reductions of over 20% on average. Additionally, it provides important insights, which for example allow assessing the benefits of acquiring more data. These results are orthogonal to other advances in the field of AutoML.","url_abs":"https://arxiv.org/abs/2111.13914v1","url_pdf":"https://arxiv.org/pdf/2111.13914v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-and-informative-model-selection-using","repo_url":"https://github.com/fmohr/lccv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2111.13914","atlas_url":"https://app.syntology.ai/?focus=2111.13914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13914"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/fmohr/lccv","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"484fef726440a398","entry":"format_learner","repo":"fmohr/lccv","repo_kind":"official","path":"python/lccv/lccv.py","file_url":"https://github.com/fmohr/lccv/blob/HEAD/python/lccv/lccv.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"484fef726440a398"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}