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.
Security Assessment
About scikit-survival
The scikit-survival skill is a comprehensive toolkit designed for survival analysis and time-to-event modeling in Python. It addresses the challenges posed by censored survival data, enabling users to perform detailed analyses on datasets where some observations are incomplete. This library is built on the robust scikit-learn framework, which enhances its usability and integration within the Python ecosystem for data science and statistical modeling.
FAQ
What types of models can I fit using scikit-survival?
You can fit Cox proportional hazards models, ensemble methods such as Random Survival Forests and Gradient Boosting models, as well as Survival Support Vector Machines.
Is scikit-survival compatible with standard Python data analysis libraries?
Yes, scikit-survival is built on scikit-learn and integrates well with other Python libraries commonly used for data analysis.
What kind of data can I analyze with scikit-survival?
You can analyze right-censored, left-censored, and interval-censored data, as well as handle competing risks.
Are there any specific requirements to use scikit-survival?
You need to have Python and the scikit-learn library installed, as scikit-survival is built on top of it.
Can I evaluate the performance of my survival models?
Yes, you can evaluate model performance using metrics such as the concordance index, Brier score, and time-dependent AUC.
Install scikit-survival
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
davila7/claude-code-templates