AlterLab scikit Survival
技能 已验证 活跃Part of the AlterLab Academic Skills suite. 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.
To enable researchers and data scientists to perform sophisticated survival analysis and time-to-event modeling with censored data using Python's scikit-survival library.
功能
- Fit Cox proportional hazards and penalized Cox models
- Implement ensemble methods like Random Survival Forests and Gradient Boosting
- Utilize Survival Support Vector Machines (SVMs)
- Perform comprehensive model evaluation (C-index, AUC, Brier Score)
- Handle competing risks analysis
- Prepare and preprocess survival data
使用场景
- Analyzing time-to-event data in clinical research, biostatistics, or reliability engineering
- Modeling patient survival with censored data
- Identifying significant predictors of event occurrence
- Evaluating and comparing different survival models
- Performing high-dimensional survival analysis with feature selection
非目标
- Performing standard classification or regression analysis
- Handling non-censored time-to-event data exclusively
- Providing interactive GUI or web-based tools
- Replacing basic data manipulation tasks outside of survival analysis context
实践
- Survival Analysis
- Time-to-Event Modeling
- Statistical Modeling
- Data Preprocessing
先决条件
- Python environment
- scikit-survival library
- scikit-learn library
Execution
- info:Pinned dependenciesWhile standard Python libraries are used, there is no explicit dependency pinning or lockfile mechanism detailed within the skill's context, which could lead to versioning issues.
安装
npx skills add AlterLab-IEU/AlterLab-Academic-Skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
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