{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:32:49Z","timestamp":1763728369787,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":0,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811951695"},{"type":"electronic","value":"9789811951701"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This chapter explores different methods to analyze the results of Hyperparameter Tuning (HPT) experiments. Four different scenarios and two different approaches are presented. On the one hand, rankings and especially consensus rankings are introduced to aggregate the results of many different HPT results. On the other hand, statistical significance analysis and power analysis are used for a detailed analysis of single algorithms and pairwise algorithm comparisons. This chapter discusses issues with sample size determination, power calculations, hypotheses, and wrong conclusions from hypothesis testing. On top of the established methods, we add and explain severity, a frequentist approach that extends the classical concept of <jats:italic>p<\/jats:italic>-values. Mayo\u2019s concept of severity offers one solution to these issues, and one might achieve even better results by applying severity.<\/jats:p>","DOI":"10.1007\/978-981-19-5170-1_5","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:46:27Z","timestamp":1672541187000},"page":"121-161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Ranking and Result Aggregation"],"prefix":"10.1007","author":[{"given":"Thomas","family":"Bartz-Beielstein","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olaf","family":"Mersmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sowmya","family":"Chandrasekaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"container-title":["Hyperparameter Tuning for Machine and Deep Learning with R"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-5170-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T03:50:28Z","timestamp":1672545028000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-5170-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789811951695","9789811951701"],"references-count":0,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-5170-1_5","relation":{},"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}