Scikit-learn: consistent estimator interfaces and reusable learning tools

What changed

Scientific scope

Integrated supervised/unsupervised learning implementations with NumPy/SciPy data structures, consistent estimator/transformer interfaces, compiled numerical components, tests and documentation for reusable Python workflows. Integration/software contribution does not reassign invention of SVMs, PCA, clustering, NumPy or linked libraries.

Evidence

The linked original sources describe this contribution and its evidence limits. WRS has not independently reconstructed the full scientific claims. Integration/software contribution does not reassign invention of SVMs, PCA, clustering, NumPy or linked libraries.

Contributors

Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Scikit-learn: Machine Learning in Python

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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