What is Feature Engineering?
TL;DR
The technique of designing and creating useful input variables (features) from raw data to improve ML model performance.
Feature Engineering: Definition & Explanation
Feature engineering is the process of designing and creating features (input variables) for machine learning models from raw data. It includes numerical normalization, categorical variable encoding, extracting day-of-week and month from dates, text vectorization, computing aggregate statistics, and creating interaction terms between variables. The saying 'success in machine learning is 80% feature engineering' reflects its massive impact on model performance. While AutoML tools are advancing automation, domain-knowledge-driven feature design remains an area where human creativity is essential.