Individual skill
scikit-learn-pro
scikit-learn guidance — pipelines, model selection, cross-validation, preprocessing, and production deployment.
skills/scikit-learn-pro/SKILL.md
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## Overview
scikit-learn is the workhorse of classical machine learning: consistent `fit`/`predict`/`transform` APIs across dozens of estimators, excellent preprocessing and model-selection tooling, and the `Pipeline` abstraction that prevents the most common ML bug (data leakage). For tabular data and problems that don't need deep learning, it's usually the right tool.
This skill covers the sklearn workflow done properly: pipelines, correct cross-validation, preprocessing, hyperparameter search, and the path from experiment to a deployed model.
## When to use
- Building classical ML models (classification, regression, clustering).
- Preventing data leakage with pipelines.
- Tuning hyperparameters (grid/random search).
- Preprocessing (scaling, encoding, imputation).
- Evaluating models correctly (cross-validation, metrics).
- Deploying sklearn models to production.