# Ds Feature Engineering

> Advanced strategies for creating model-ready features while avoiding data leakage.

- Skill: `jcorpac/ds-feature-engineering` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jcorpac/ds-feature-engineering`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcorpac/ds-feature-engineering/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jcorpac (https://skillmd.com/u/jcorpac)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jcorpac/ds-feature-engineering

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# 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`.