{"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/hyperparameter-importance-across-datasets","title":"Hyperparameter Importance Across Datasets","arxiv_id":"1710.04725","date":"2017-10-12","proceeding":null,"authors":["J. N. van Rijn","F. Hutter"],"abstract":"With the advent of automated machine learning, automated hyperparameter\noptimization methods are by now routinely used in data mining. However, this\nprogress is not yet matched by equal progress on automatic analyses that yield\ninformation beyond performance-optimizing hyperparameter settings. In this\nwork, we aim to answer the following two questions: Given an algorithm, what\nare generally its most important hyperparameters, and what are typically good\nvalues for these? We present methodology and a framework to answer these\nquestions based on meta-learning across many datasets. We apply this\nmethodology using the experimental meta-data available on OpenML to determine\nthe most important hyperparameters of support vector machines, random forests\nand Adaboost, and to infer priors for all their hyperparameters. The results,\nobtained fully automatically, provide a quantitative basis to focus efforts in\nboth manual algorithm design and in automated hyperparameter optimization. The\nconducted experiments confirm that the hyperparameters selected by the proposed\nmethod are indeed the most important ones and that the obtained priors also\nlead to statistically significant improvements in hyperparameter optimization.","url_abs":"http://arxiv.org/abs/1710.04725v2","url_pdf":"http://arxiv.org/pdf/1710.04725v2.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":"hyperparameter-importance-across-datasets","repo_url":"https://github.com/janvanrijn/openml-pimp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"hyperparameter-importance-across-datasets","repo_url":"https://github.com/luxusg1/metabu","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.04725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.04725"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. 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