We modify traditional mean target encoding (with M-estimate regularization) to incorporate the quantile instead of the mean. This helps to provide a boost in machine learning models, especially when using Generalized Linear Models and measuring Mean Absolute Error (MAE.
Our proposal outperforms state-of-the-art encoders, including the traditional statistical mean target encoder, when considering the Mean Absolute Error, especially in the presence of long-tailed or skewed distributions.
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