scikit-learn
Covers classical machine learning in Python with scikit-learn (sklearn): classification and regression estimators, clustering and dimensionality reduction, preprocessing, Pipeline and ColumnTransformer, cross-validation, metrics, and GridSearchCV hyperparameter tuning. Use when training or comparing classifiers and regressors on tabular or text data, clustering data and choosing the cluster count, building leakage-free preprocessing pipelines, evaluating models with cross-validation and metrics, or tuning hyperparameters. Not for deep learning; use a dedicated neural network framework instead.
- Version
- 1.3
- 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 df088027ff23, 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.
- Read
- Write
- Edit
- Bash
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
Every link opens the file at its source, pinned to the revision this page describes.