Agent skill · research science · aiskillstore
scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Why this skill is useful
Provides specific domain knowledge and examples for using scikit-learn in machine learning tasks that the AI might not reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, scikit-learn, seaborn installed locally. About 7k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 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 Complete Pipeline with Mixed Data Core Capabilities 1. Supervised Learning Comprehensive algorithms for classification and regression tasks. Key algorithms: Linear models: Logistic Regression, Linear Regression, Ridge, Lasso, ElasticNet Tree-based: Decision Trees, Random Forest, Gradient Boosting Support Vector Machines: SVC, SVR with various kernels Ensemble methods: AdaBoost, Voting, Stacking Neural Networks: MLPClassifier, MLPRegressor Others: Naive Bayes, K-Nearest Neighbors When to use: Classification: Predicting discrete categories (spam detection, image classification, fraud detection) Regression: Predicting continuous values (price prediction, demand forecasting) See: references/supervisedlearning.md for detailed algorithm documentation, parameters, and usage examples. 2. Unsupervised Learning Discover patterns in unlabeled data through clustering and dimensionality reduction. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/scikit-learn