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.
- Version
- 1.2
- License
- BSD-3-Clause license
- Compatibility
- Requires Python 3.11+ and scikit-learn 1.7+. NumPy and SciPy are required dependencies. Optional matplotlib/seaborn for bundled example scripts that save plots.
Pinned to revision de66e10cd0c8, 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/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
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