Feature Engineering Studio

Feature engineering is often where the most significant model performance gains are found.

Core Techniques

• Encoding: One-Hot (low cardinality) vs. Target Encoding (high cardinality).
• Scaling: StandardScaler (Gaussian data) vs. RobustScaler (data with outliers).
• Binning: Converting numerical values into categorical ranges (e.g., Age -> Age Group).
• Interaction Terms: Multiplying features together to capture combined effects.

Safety: Avoiding Data Leakage

• CRITICAL: Never calculate scaling parameters (mean, std) on the test set.
• Fit your encoders/scalers ONLY on the training data and use them to transform both train and test.

Scikit-Learn Pipeline Integration

Implement custom transformers using BaseEstimator and TransformerMixin for seamless integration into sklearn.pipeline.Pipeline.