@davila7/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.
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Everything this skill can do — files, network, commands, and more.
No demos yet. To add one, ask your AI agent: "Submit a demo for @davila7/scikit-survival" — or upload via the API.
To add a demo, ask your AI agent: "Submit a demo for @davila7/scikit-survival"
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