The Challenges of Creating Features for Machine Learning - KDnuggets

When I decided to leave academia and re-train as a data scientist, I quickly found out that I had to learn R or Python, or well… both. That's probably the first time I heard about Python. I never imagined that 3 years later I would be maintaining an increasingly popular open source Python library for feature engineering: Feature-engine. In this article, I want to discuss the challenges of feature engineering and selection both from the technical and operational side, and then lay out how Feature-engine, an open source Python library, can help us mitigate those challenges. I will also highlight the advantages and shortcomings of Feature-engine in the context of other Python libraries. Feature-engine is an open-source Python library for feature engineering and feature selection. It works like Scikit-learn, with methods fit() and transform() that learn parameters from the data and then use those parameters to transform the data.