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Predictive modeling is a formula that transforms a list of input fields or variables into some output of interest. Feature engineering is simply a thoughtful creation of new input fields from existing input fields, either in an automated fashion or manually, with valuable inputs from domain expertise, logical reasoning, or intuition. The new input fields could result in better inferences and insights from data and exponentially increase the performance of predictive models.
Feature engineering is one of the most important parts of the data preparation process, where deriving new and meaningful variables takes place. Feature engineering enhances and enriches the ingredients needed for creating a robust model. Many times, it is the key differentiator between an average and a good model.
Learn some of the common and popular tricks employed for feature engineering by reading Senior Data Scientist, Jacob Joseph’s guest article on Data-Informed .