# Ml Supervised Patterns

> Professional patterns for supervised learning pipelines, including classification and regression.

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

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# ML Supervised Patterns

Supervised learning is the core of predictive modeling. This skill focuses on building robust, scalable pipelines using Scikit-Learn.

## Pipeline Architecture
1.  **Imputation**: Handling missing values (`SimpleImputer`).
2.  **Encoding**: Transforming categorical data (`OneHotEncoder`).
3.  **Scaling**: Normalizing numerical features (`StandardScaler`).
4.  **Estimator**: The model (e.g., `RandomForestClassifier`, `XGBRegressor`).

## Best Practices
- **Never Leak Data**: Split into train/test before any preprocessing.
- **Cross-Validation**: Use `cross_val_score` or `GridSearchCV` to ensure generalization.
- **Fairness**: Check for bias in training data.

## Tools
- `Scikit-Learn`: The industry standard for tabular data.
- `XGBoost` / `LightGBM`: High-performance gradient boosting.