Key steps in the feature engineering process
Machine learning may seem magical at times, but it's not. A lot of effort goes into ensuring algorithms perform properly and it starts with getting data sets in usable shape. That's where the feature engineering process comes in. The feature engineering process is key to understanding what data is available to use in a machine learning algorithm. Features are also necessary to test how accurate models are and further improving their accuracy. Feature engineering is the process of taking raw data and transforming it into features that can be used in machine learning algorithms. Features are the specific units of measurement that algorithms evaluate for correlations. According to Brian Lett, research director at Dresner Advisory Services, feature engineering is a balance of art and science. The art side incorporates domain expertise, while the science side finds the correct variables.