Feature Engineering Toolkit

Use this skill when the main question is how to improve or restructure the input features.

Overview

This skill covers feature creation, encoding, scaling coordination, and feature selection before the model is finalized.

When to Use This Skill

• Creating derived variables, interaction terms, bins, encodings, or date-based features
• Selecting or pruning features before training
• Reworking feature representations to fit model assumptions or data geometry

Not For / Boundaries

• Full training runs and benchmark ownership: use training-machine-learning-models
• Post-hoc interpretation of a trained model: use feature-importance-analyzer
• Leak checking across the preprocessing order: use ml-data-leakage-guard

Typical Outputs

• Candidate feature set changes
• Implementation notes for encoders, scalers, and selectors
• Rationale for what to keep, drop, or combine

Related Skills

• data-normalization-tool for scaling-only questions
• ml-data-leakage-guard before accepting the engineered pipeline