feature-engineering

What problem does it solve?

This Skill addresses the challenge of improving machine learning model performance by systematically generating, evaluating, and selecting relevant features, while rigorously preventing data leakage.

Core Features & Use Cases

• Leakage Prevention: Provides strict rules and patterns to avoid common data leakage pitfalls.
• Diverse Feature Recipes: Offers pre-built scripts for numerical, categorical, and temporal feature engineering.
• SHAP-based Importance & Pruning: Integrates SHAP values for feature importance and a disciplined pruning process to retain only impactful features.
• Use Case: After establishing a baseline model, use this Skill to generate advanced features like target-encoded categoricals (safely within CV folds), lag features for time-series data, and interaction terms, then prune them based on SHAP importance and OOF score improvement.

Quick Start

Use the feature-engineering skill to add numerical and categorical features to your dataset, then evaluate their importance using SHAP.