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Found 3 Skills
Advanced sub-skill for scikit-learn focused on model interpretability, feature importance, and diagnostic tools. Covers global and local explanations using built-in inspection tools and SHAP/LIME integrations.
Independent model QA expert who audits ML and statistical models end-to-end - from documentation review and data reconstruction to replication, calibration testing, interpretability analysis, performance monitoring, and audit-grade reporting.
Machine Learning provides end-to-end ML pipeline construction with PyTorch and scikit-learn, covering model selection, training, evaluation, interpretability, hyperparameter tuning, and experiment tracking.