  Regression problems have been widely studied in machinelearning literature
resulting in a plethora of regression models and performance measures. However,
there are few techniques specially dedicated to solve the problem of how to
incorporate categorical features to regression problems. Usually, categorical
feature encoders are general enough to cover both classification and regression
problems. This lack of specificity results in underperforming regression
models. In this paper,we provide an in-depth analysis of how to tackle high
cardinality categor-ical features with the quantile. Our proposal outperforms
state-of-the-encoders, including the traditional statistical mean target
encoder, when considering the Mean Absolute Error, especially in the presence
of long-tailed or skewed distributions. Besides, to deal with possible
overfitting when there are categories with small support, our encoder benefits
from additive smoothing. Finally, we describe how to expand the encoded values
by creating a set of features with different quantiles. This expanded encoder
provides a more informative output about the categorical feature in question,
further boosting the performance of the regression model.
