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Since most machine learning (ML) algorithms are designed for numericalinputs, efficiently encoding categorical variables is a crucial aspect in dataanalysis. A common problem are high cardinality features, i.e. unorderedcategorical predictor variables with a high number of levels. We studytechniques that yield numeric representations of categorical variables whichcan then be used in subsequent ML applications. We focus on the impact of thesetechniques on a subsequent algorithm's predictive performance, and -- ifpossible -- derive best practices on when to use which technique. We conducteda large-scale benchmark experiment, where we compared different encodingstrategies together with five ML algorithms (lasso, random forest, gradientboosting, k-nearest neighbors, support vector machine) using datasets fromregression, binary- and multiclass- classification settings. In our study,regularized versions of target encoding (i.e. using target predictions based onthe feature levels in the training set as a new numerical feature) consistentlyprovided the best results. Traditionally widely used encodings that makeunreasonable assumptions to map levels to integers (e.g. integer encoding) orto reduce the number of levels (possibly based on target information, e.g. leafencoding) before creating binary indicator variables (one-hot or dummyencoding) were not as effective in comparison.

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features