Agent skill · research science · k-dense-ai
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 examples of building ML pipelines and model evaluation techniques that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, scikit-learn, scipy, seaborn installed locally. About 5k tokens when loaded. Last updated 2026-08-03. 32,849 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 Tested against scikit-learn 1.8.0 (stable; December 2025). Requires Python 3.11–3.14 (free-threaded CPython 3.14 wheels available in 1.8+). Install the PyPI package scikit-learn (not the deprecated sklearn package on PyPI). Import in code as sklearn. Check your version: 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 Five capability areas are documented in references/corecapabilities.md, with per-topic detail in references/supervisedlearning.md, references/unsupervisedlearning.md, references/modelevaluation.md, references/preprocessing.md, and references/pipelinesandcomposition.md: 1. Supervised learning — classification and regression estimator families. 2. Unsupervised learning — clustering, decomposition, and manifold learning. 3. Model evaluation and selection — metrics, cross-validation, and hyperparameter search. 4. Data preprocessing — scaling, encoding, imputation, and feature selection. 5. Pipelines and composition — Pipeline and ColumnTransformer. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/scikit-learn