Feature Engineering

Overview

Feature engineering creates and transforms features to improve model performance, interpretability, and generalization through domain knowledge and mathematical transformations.

When to Use

• When you need to improve model performance beyond using raw features
• When dealing with categorical variables that need encoding for ML algorithms
• When features have different scales and require normalization
• When creating domain-specific features based on business knowledge
• When handling skewed distributions or non-linear relationships
• When preparing data for different types of ML algorithms with specific requirements

Engineering Techniques

• Encoding: Converting categorical to numerical
• Scaling: Normalizing feature ranges