scikit-survival: Survival Analysis in Python

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Last evaluated: March 29, 2026

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---
name: scikit-survival
description: 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...

Full prompt length: 14962 characters

Tools & Technologies

• python
• Python