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.
8.3
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0
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Machine Learning
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Exceptional scikit-learn skill with comprehensive coverage of ML workflows. The description clearly enables CLI agent invocation across supervised/unsupervised learning, preprocessing, pipelines, and evaluation. Task knowledge is outstanding with complete code examples, workflows, and referenced documentation files. Structure is excellent with a well-organized SKILL.md providing overview and indexing to detailed references, though slightly verbose. Novelty is strong - while scikit-learn documentation exists, this skill packages end-to-end workflows, best practices, and production patterns that would require significant tokens for an agent to synthesize correctly, especially for pipeline composition and preventing data leakage.
K-Dense-AI
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