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A common problem are high cardinality features, i.e.\u00a0unordered categorical predictor variables with a high number of levels. We study techniques that yield numeric representations of categorical variables which can then be used in subsequent ML applications. We focus on the impact of these techniques on a subsequent algorithm\u2019s predictive performance, and\u2014if possible\u2014derive best practices on when to use which technique. We conducted a large-scale benchmark experiment, where we compared different encoding strategies together with five ML algorithms (lasso, random forest, gradient boosting,<jats:italic>k<\/jats:italic>-nearest neighbors, support vector machine) using datasets from regression, binary- and multiclass\u2013classification settings. In our study, regularized versions of target encoding (i.e.\u00a0using target predictions based on the feature levels in the training set as a new numerical feature) consistently provided the best results. Traditionally widely used encodings that make unreasonable assumptions to map levels to integers (e.g.\u00a0integer encoding) or to reduce the number of levels (possibly based on target information, e.g.\u00a0leaf encoding) before creating binary indicator variables (one-hot or dummy encoding) were not as effective in comparison.<\/jats:p>","DOI":"10.1007\/s00180-022-01207-6","type":"journal-article","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T13:27:02Z","timestamp":1646400422000},"page":"2671-2692","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":137,"title":["Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2388-553X","authenticated-orcid":false,"given":"Florian","family":"Pargent","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8867-762X","authenticated-orcid":false,"given":"Florian","family":"Pfisterer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4511-6245","authenticated-orcid":false,"given":"Janek","family":"Thomas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6002-6980","authenticated-orcid":false,"given":"Bernd","family":"Bischl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"1207_CR1","unstructured":"Bates D (2020) Computational methods for mixed models. 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