  Feature preprocessing continues to play a critical role when applying machine
learning and statistical methods to tabular data. In this paper, we propose the
use of the kernel density integral transformation as a feature preprocessing
step. Our approach subsumes the two leading feature preprocessing methods as
limiting cases: linear min-max scaling and quantile transformation. We
demonstrate that, without hyperparameter tuning, the kernel density integral
transformation can be used as a simple drop-in replacement for either method,
offering protection from the weaknesses of each. Alternatively, with tuning of
a single continuous hyperparameter, we frequently outperform both of these
methods. Finally, we show that the kernel density transformation can be
profitably applied to statistical data analysis, particularly in correlation
analysis and univariate clustering.
