Machine learning is increasingly moving from hand-designed models to

automatically optimized pipelines using tools such as H20, TPOT, and auto-

sklearn. These libraries, along with methods such as random search, aim to

simplify the model selection and tuning parts of machine learning by finding the

best model for a dataset with little to no manual intervention. However,

feature engineering, an arguably more valuable aspect of the machine learning

pipeline, remains almost entirely a human labor.

Typically, feature engineering is a drawn-out manual process, relying on domain

knowledge, intuition, and data manipulation. This process can be extremely

tedious and the final features will be limited both by human subjectivity and

time. Automated feature engineering aims to help the data scientist by

automatically creating many candidate features out of a dataset from which

the best can be selected and used for training.