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
scikit-survival 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 robust analyses even when data is incomplete or partially known. The library is built on top of scikit-learn, making it a familiar choice for users who are already experienced with this popular machine learning framework. By providing specialized methods for various modeling techniques, scikit-survival empowers researchers and data scientists to derive meaningful insights from their survival data.
FAQ
What types of survival models can I fit using scikit-survival?
You can fit Cox proportional hazards models, ensemble methods like Random Survival Forests and Gradient Boosting, as well as Survival Support Vector Machines.
Is scikit-survival compatible with scikit-learn?
Yes, scikit-survival is built on top of scikit-learn and is designed to integrate seamlessly with its tools and methodologies.
What types of censored data can I work with?
scikit-survival supports right-censored, left-censored, and interval-censored data.
Are there any specific requirements for using scikit-survival?
You need to have Python installed along with the scikit-learn library for scikit-survival to function properly.
Can I evaluate the performance of my survival models?
Yes, scikit-survival provides methods to evaluate model performance using metrics like the concordance index and Brier score.
Install scikit-survival
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
K-Dense-AI/claude-scientific-skills