Feature Engineering

Feature engineering is the process of creating new features from raw data to increase the predictive power of the learning algorithm. Engineered features should capture additional information that is not easily apparent in the original feature set; While feature selection is the process of selecting the key subset of features to reduce the dimensionality of the training problem.

Normally feature engineering is applied first to generate additional features, and then feature selection is done to eliminate irrelevant, redundant, or highly correlated features.

Feature engineering and selection are part of the modeling stage of the Team Data Science Process (TDSP).