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scikit-survival is a specialized extension for scikit-learn that enables advanced time-to-event analysis for datasets with censored observations. This skill empowers Claude to build, train, and evaluate survival models ranging from traditional Cox Proportional Hazards to complex ensemble methods like Random Survival Forests and Gradient Boosting. It provides structured workflows for data preparation, model selection, performance evaluation using concordance indices, and handling competing risks, making it an essential tool for clinical research, churn prediction, and reliability engineering.
