spb/tools-spboucher-ai

1{2  "slug": "scikit-learn",3  "name": "scikit-learn",4  "aliases": ["sklearn"],5  "vendor": { "name": "scikit-learn developers", "slug": "scikit-learn", "website": "https://scikit-learn.org" },6  "homepage_url": "https://scikit-learn.org/",7  "docs_url": "https://scikit-learn.org/stable/",8  "repo_url": "https://github.com/scikit-learn/scikit-learn",9  "license_url": "https://github.com/scikit-learn/scikit-learn/blob/main/COPYING",10  "product_type": "library",11  "product_status": "active",12  "summary": "BSD-licensed Python machine-learning library for classification, regression, clustering, dimensionality reduction, preprocessing and model selection on NumPy and SciPy",13  "description": "scikit-learn is the standard Python library for classical (non-deep) machine learning, released under the BSD 3-Clause license and built on NumPy, SciPy and Matplotlib. It implements supervised methods (linear and logistic regression, ridge and lasso, support vector machines, nearest neighbours, decision trees, random forests, gradient boosting), unsupervised methods (k-means, HDBSCAN, hierarchical clustering, PCA, non-negative matrix factorization, manifold learning), preprocessing and feature extraction, and model-selection tooling such as pipelines, cross-validation, grid and randomized search and a broad set of metrics. Its uniform fit/predict/transform API has become a de facto interface that other libraries, including EconML and many time-series packages, adopt.\n\nIn research workflows scikit-learn supplies the nuisance-function learners for double machine learning, the prediction baselines in forecasting comparisons and the preprocessing in text and tabular pipelines. It is a CPU library for datasets that fit in memory; deep learning and GPU training belong to PyTorch or JAX, and statistical inference (standard errors, tests) is the domain of statsmodels. Version 1.9.1 is current at the time of writing.",14  "capabilities": ["Supervised learning: linear models, SVMs, trees, ensembles, nearest neighbours", "Clustering and dimensionality reduction", "Preprocessing, feature extraction and imputation", "Pipelines, cross-validation and hyperparameter search", "Metrics and model evaluation", "Consistent estimator API adopted by other libraries"],15  "workflows": ["Nuisance models for double/debiased machine learning", "Predictive benchmark against hedonic or econometric models", "Text feature extraction for document classification"],16  "advantages": ["Consistent, well-documented API", "BSD licence and large community", "Interoperates with pandas and the scientific stack"],17  "limitations": ["No deep learning or GPU support", "Limited statistical inference output", "In-memory, single-node"],18  "prerequisites": ["Python with NumPy and SciPy"],19  "audiences": ["Data scientists", "Researchers using ML for prediction or nuisance estimation", "Students learning machine learning"],20  "alternatives": ["statsmodels", "pytorch", "econml", "jax"],21  "comparison_notes": "statsmodels provides inference where scikit-learn provides prediction; PyTorch and JAX cover deep learning and GPU workloads; EconML builds causal estimators on scikit-learn's API.",22  "categories": [{ "slug": "math-stats-tools", "primary": true }, { "slug": "model-training" }],23  "tags": ["python", "machine-learning", "open-source", "statistics"],24  "use_cases": ["estimate-causal-effects", "fine-tune-models"],25  "operating_systems": ["macos", "linux", "windows"],26  "execution_modes": ["local"],27  "interfaces": ["library"],28  "deployment_models": ["embedded"],29  "integrations": ["NumPy", "SciPy", "pandas", "Matplotlib", "EconML", "joblib"],30  "data_formats": ["NumPy arrays", "pandas DataFrame", "SciPy sparse matrices"],31  "languages": ["Python", "Cython", "C++"],32  "source_model": "open-source",33  "license_spdx": "BSD-3-Clause",34  "pricing_model": ["free", "open-source"],35  "self_hostable": true,36  "sources": [37    { "url": "https://scikit-learn.org/", "type": "official_site", "title": "scikit-learn: machine learning in Python", "supports": ["name", "summary"], "checked_at": "2026-11-02" },38    { "url": "https://scikit-learn.org/stable/", "type": "official_docs", "title": "scikit-learn 1.9.1 documentation", "supports": ["capabilities", "description"], "checked_at": "2026-11-02" },39    { "url": "https://github.com/scikit-learn/scikit-learn/blob/main/COPYING", "type": "license_page", "title": "scikit-learn COPYING", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02" }40  ],41  "confidence": "high",42  "discovery_method": "editorial-research",43  "depth": "standard",44  "editorial_status": "reviewed",45  "visibility": "public",46  "featured": false,47  "author": "batch-E"48}49