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

Understand

Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis.

• A common problem are high cardinality features, i.e.
• unordered 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.