scikit-learn
Supports machine learning in Python with scikit-learn. Applies 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.
- Version
- 1.5
- License
- BSD-3-Clause license
- Compatibility
- Requires Python 3.11+ and scikit-learn 1.9.1. NumPy, SciPy, and joblib are dependencies; bundled scripts also require pandas and matplotlib. Installation needs network access; bundled examples use local datasets without credentials.
Pinned to revision 68105dd992f1, so it is the text this page describes rather than whatever the author pushed since.
Pre-approved tools experimental
Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.
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Files
- skills/scikit-learn/SKILL.md
- skills/scikit-learn/SKILL_CN.md
- skills/scikit-learn/references/common_workflows.md
- skills/scikit-learn/references/core_capabilities.md
- skills/scikit-learn/references/model_evaluation.md
- skills/scikit-learn/references/pipelines_and_composition.md
- skills/scikit-learn/references/preprocessing.md
- skills/scikit-learn/references/quick_reference.md
- skills/scikit-learn/references/supervised_learning.md
- skills/scikit-learn/references/unsupervised_learning.md
- skills/scikit-learn/scripts/classification_pipeline.py
- skills/scikit-learn/scripts/clustering_analysis.py
Every link opens the file at its source, pinned to the revision this page describes.