from Awesome Skills (CN)98
Comprehensive scikit-learn guidance for classical machine learning: supervised/unsupervised algorithms, preprocessing, pipelines, evaluation and production-read
This skill provides an extensive reference and actionable guidance for using scikit-learn: algorithm selection (classification, regression, ensembles), preprocessing, pipelines, hyperparameter tuning, model evaluation and common production workflows. It includes example scripts and detailed best-practices for reproducible ML.
Use this skill when building or evaluating classical ML models in Python: training classifiers/regressors, preparing data, building pipelines for production, tuning hyperparameters, or performing clustering and dimensionality reduction. It's intended for data scientists, ML engineers, and developers integrating ML into products.
scripts/ directory for classification and clustering workflows (has_scripts=true)references/ docs covering supervised learning, preprocessing, pipelines, evaluation and more (has_references=true)Best used in agents that can provide code snippets, run or reference example scripts, and guide users through ML workflows (developer-facing assistants, coding copilots).
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