  Feature engineering is of critical importance in the field of Data Science.
While any data scientist knows the importance of rigorously preparing data to
obtain good performing models, only scarce literature formalizes its benefits.
In this work, we will present the method of Statistically Enhanced Learning
(SEL), a formalization framework of existing feature engineering and extraction
tasks in Machine Learning (ML). The difference compared to classical ML
consists in the fact that certain predictors are not directly observed but
obtained as statistical estimators. Our goal is to study SEL, aiming to
establish a formalized framework and illustrate its improved performance by
means of simulations as well as applications on real life use cases.
