domains / families / science / scikit-survival
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library. Builds, evaluates, and audits right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.
The K-Dense-AI copy is the most rigorous, providing a strict, leakage-safe workflow, pinned dependencies, and specific constraints (e.g., IPCW metrics must use training data for censoring distributions, and evaluation times must be restricted to the training support). The other three copies (Vibe-Skills, medical-research-skills, OpenClaw-Medical-Skills) are nearly identical to each other and provide a broader, more introductory overview with a model selection decision tree and a complete runnable example using a built-in dataset. However, they lack the strict procedural safeguards found in the K-Dense copy. Because the K-Dense copy is a specialized, high-integrity procedure while the others are general guides that lack these critical constraints, crowning one would hide the alternative approach. A combined skill should use the K-Dense workflow as the core procedure but include the model selection heuristics and the runnable example from the other copies.