Why Features Matter More Than Algorithms
A simple logistic regression model with well-engineered features often outperforms a complex deep learning model trained on raw, unprocessed data. Feature engineering is the art of extracting signal from noise.
Key Techniques
- Encoding Categoricals: One-hot encoding for low-cardinality features; target encoding for high-cardinality features.
- Datetime Decomposition: Extract hour, day of week, month, is_weekend, and days_since_last_event from timestamps.
- Interaction Features: Multiply or divide two features to capture non-linear relationships.
- Lag Features: For time-series data, include values from previous time steps as features.