AlterLab scikit Survival
Skill Verifiziert AktivPart 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.
Funktionen
- 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
Anwendungsfälle
- 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
Nicht-Ziele
- 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
Praktiken
- Survival Analysis
- Time-to-Event Modeling
- Statistical Modeling
- Data Preprocessing
Voraussetzungen
- 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.
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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