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This skill equips Claude with specialized knowledge for performing survival analysis in Python, leveraging the scikit-survival library built on top of scikit-learn. It enables the creation of sophisticated time-to-event models—ranging from traditional Cox proportional hazards and penalized regression to advanced machine learning approaches like Random Survival Forests and Gradient Boosting. The skill provides expert guidance on handling the unique challenges of censored data, analyzing competing risks, and implementing specialized evaluation metrics such as Uno's C-index and Brier scores to ensure robust predictive performance in clinical, industrial, or business contexts.
