{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:08:41Z","timestamp":1742972921106,"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 describes the special quality requirements placed on official statistics and builds a bridge to the tuning of hyperparameters in Machine Learning (ML). To carry out the latter optimally under consideration of constraints and to assess its quality is part of the tasks of the employees entrusted with this work. The chapter sheds special light on open questions and the need for further research.<\/jats:p>","DOI":"10.1007\/978-981-19-5170-1_7","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:46:27Z","timestamp":1672541187000},"page":"177-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hyperparameter Tuning in German Official Statistics"],"prefix":"10.1007","author":[{"given":"Florian","family":"Dumpert","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Schmidt","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_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T03:50:37Z","timestamp":1672545037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-5170-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789811951695","9789811951701"],"references-count":0,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-5170-1_7","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"}}]}}