This is your go-to for classical machine learning tasks when you need something interpretable and battle-tested. Covers the full workflow: train/test splits, preprocessing with StandardScaler and OneHotEncoder, classification with RandomForest or GradientBoosting, clustering with KMeans or DBSCAN, and proper evaluation with cross-validation. The Pipeline and ColumnTransformer examples are especially useful since they prevent data leakage and make it easy to handle mixed numeric and categorical features in one workflow. Skip this if you're doing deep learning, but for tabular data, customer segmentation, or any project where you need to explain your model to stakeholders, scikit-learn remains the standard for good reason.

skills.sh installs
944
GitHub stars
33.2K
last updated
Aug 21, 2026

Install

$ npx -y skills add davila7/claude-code-templates --skill scikit-learn --agent claude-code

Installs into .claude/skills of the current project.

Files

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

When to Use This Skill

Use the scikit-learn skill when:

  • Building classification or regression models
  • Performing clustering or dimensionality reduction
  • Preprocessing and transforming data for machine learning
  • Evaluating model performance with cross-validation
  • Tuning hyperparameters with grid or random search
  • Creating ML pipelines for production workflows
  • Comparing different algorithms for a task
  • Working with both structured (tabular) and text data
  • Need interpretable, classical machine learning approaches

Quick Start

Classification Example

Sponsors

Details

Repository
github.com/davila7/claude-code-templates
Categories
Documentation, Data Science & ML, Python
First seen
May 16, 2026

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