Entropy

Feature Engineering and Selection

Feature engineering is the process of transforming raw data into meaningful features that improve model performance. This includes creating new features from existing ones, selecting the most relevant features, and reducing dimensionality.

Feature selection techniques such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and Mutual Information help identify the most important attributes. Proper feature engineering ensures that models learn from the most relevant data while reducing computational complexity.