scikit-learn
Classical machine learning with scikit-learn for classification, regression, clustering, and preprocessing.
- Covers supervised learning (linear models, trees, SVMs, ensembles, neural networks), unsupervised learning (K-Means, DBSCAN, PCA, t-SNE), and model evaluation with cross-validation and hyperparameter tuning
- Includes preprocessing transformers for scaling, encoding categorical variables, imputing missing values, and feature engineering
- Provides Pipeline and ColumnTransformer for building reproducible workflows that prevent data leakage and handle mixed data types
- Supports both tabular and text data with interpretable, classical ML approaches suitable for production deployments
Scikit-learn
Overview
This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.
Installation
# Install scikit-learn using uv
uv uv pip install scikit-learn
# Optional: Install visualization dependencies
uv uv pip install matplotlib seaborn
# Commonly used with
uv uv pip install pandas numpy
More from davila7/claude-code-templates
senior-data-scientist
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
2.6Ksenior-backend
Comprehensive backend development skill for building scalable backend systems using NodeJS, Express, Go, Python, Postgres, GraphQL, REST APIs. Includes API scaffolding, database optimization, security implementation, and performance tuning. Use when designing APIs, optimizing database queries, implementing business logic, handling authentication/authorization, or reviewing backend code.
2.1Kexcel analysis
Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.
1.5Kliterature-review
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
1.5Ksenior-frontend
Comprehensive frontend development skill for building modern, performant web applications using ReactJS, NextJS, TypeScript, Tailwind CSS. Includes component scaffolding, performance optimization, bundle analysis, and UI best practices. Use when developing frontend features, optimizing performance, implementing UI/UX designs, managing state, or reviewing frontend code.
1.5Kmarket-research-reports
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter's Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.
1.3K