What is Automated Feature Engineering?

Feature engineering is the process of using domain knowledge to extract features from raw data through data mining techniques. Feature engineering is more an art than a science in that it uses domain knowledge of the data to create features that increase the predictive power of machine learning algorithms. Data Engineering will allow you to represent the underlying structure of the data accurately and therefore create the best model. Features can be engineered by decomposing or splitting features, from external data sources, or aggregating or combining features to create new features. Automated Feature Engineering is a process of preparing a dataset for machine learning by changing features or deriving new features to improve machine learning model performance. It is one of the fundamental blocks of Tibil’s iDEA framework and data analytics platform where we transform the customer’s data through our in-depth knowledge of our customer’s domain.