Classical machine learning in Python with scikit-learn — algorithms, preprocessing, pipelines, and best-practice reference documentation. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, feature preprocessing, or building ML pipelines.
Install
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scikit-learnnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scikit-learn -a claude-code -gnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scikit-learn -a codex -ggemini skills install https://github.com/AlterLab-IEU/AlterLab-Academic-Skills.git --path skills/data-science/alterlab-scikit-learngh skill install AlterLab-IEU/AlterLab-Academic-Skills alterlab-scikit-learngit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-Skills /tmp/AlterLab-IEU__AlterLab-Academic-Skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-scikit-learn ~/.claude/skills/alterlab-scikit-learn # Claude Code
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-scikit-learn ~/.agents/skills/alterlab-scikit-learn # Codex, Cursor, Gemini CLI, Copilot, OpenCode