
from superpowers14
Comprehensive, practical reference for classical machine learning workflows using scikit-learn: preprocessing, pipelines, supervised and unsupervised algorithms
This skill provides a complete, practical reference for using scikit-learn to build, evaluate, and deploy classical machine learning models. It bundles clear guidance and example scripts for supervised learning (classification/regression), unsupervised learning (clustering/dimensionality reduction), preprocessing, model evaluation, hyperparameter tuning, and production-ready pipelines. The content includes end-to-end examples, recommended pipelines, and reference documents for common algorithms and metrics.
Use this skill when you need to: train or compare classical ML algorithms; construct reproducible pipelines that include preprocessing and encoding; evaluate models with robust cross-validation strategies; tune hyperparameters; or prepare models for production. It is useful for data scientists prototyping models, engineers operationalising pipelines, and learners needing concise, copy-pasteable templates.
Works well as a knowledge/reference skill for code-capable agents (Copilot/Codex/Claude Code/Gemini-like agents) that can run or suggest Python examples.
This skill has not been reviewed by our automated audit pipeline yet.
Ip2location IO Automation (Rube MCP / Composio)
Automate Ip2location IO workflows through Rube MCP and Composio toolkits — discover tools, manage connections, and execute location-data operations reliably.
Skyfire Automation (Composio/Rube MCP)
Automate Skyfire operations through Composio's Skyfire toolkit using Rube MCP; discover tools, verify connections, and execute schema-compliant workflows.
Kickbox Automation (Rube MCP)
Automate Kickbox operations via the Composio Kickbox toolkit on a Rube MCP server — discover tools, manage connections, and execute Kickbox workflows safely.