{"enrichment":{"faq":[{"a":"scikit-survival is a Python library for survival analysis that handles censored data in time-to-event modeling. It offers Cox proportional hazards models, ensemble methods like Random Survival Forests and Gradient Boosting, and Survival SVMs, with evaluation metrics including concordance index and Brier score. Use it for competing risks analysis, Kaplan-Meier estimation, and any survival analysis workflow.","q":"What is scikit-survival used for in survival analysis?"},{"a":"scikit-survival provides specialized preprocessing and validation tools for survival datasets with proper censoring handling. It supports right-censored data structures and integrates censoring information directly into model training and evaluation, ensuring accurate time-to-event predictions even when some subjects' event times are unknown or incomplete.","q":"How does scikit-survival handle censored data analysis?"},{"a":"scikit-survival enables comparison of multiple survival model types including Cox proportional hazards regression, Random Survival Forests, Gradient Boosting survival models, and Survival SVMs. You can evaluate each model's performance to identify the best approach for your specific time-to-event prediction task.","q":"What survival model types can scikit-survival compare?"},{"a":"scikit-survival calculates key survival metrics: concordance index (C-index) for ranking accuracy, Brier score for prediction error, and time-dependent AUC for assessing discrimination at specific time points. These metrics enable rigorous evaluation of survival model performance on censored data.","q":"Which evaluation metrics does scikit-survival calculate?"},{"a":"Yes, scikit-survival supports non-parametric survival curve estimation through Kaplan-Meier and Nelson-Aalen estimators. It also handles competing risks analysis, allowing you to model scenarios where multiple distinct event types can occur and may prevent observation of other events.","q":"Can scikit-survival estimate non-parametric survival curves?"},{"a":"scikit-survival provides tutorials and documentation for time-to-event modeling workflows. Start by preprocessing your censored dataset, choose a model type (Cox, ensemble, or SVM), train it on your data, then evaluate using concordance index or Brier score. The library integrates with scikit-learn conventions for familiar usage patterns.","q":"How do I get started with scikit-survival for time to event modeling?"}],"shadow_tags":["event-prediction","censoring-handling","medical-statistics","risk-scoring","parametric-modeling","non-parametric-methods","prognostic-models","time-series-classification"],"summary_rewrite":"scikit-survival is a Python library for survival analysis that handles censored data in time-to-event modeling. It offers Cox proportional hazards models, ensemble methods like Random Survival Forests and Gradient Boosting, and Survival SVMs, with evaluation metrics including concordance index and Brier score. Use it for competing risks analysis, Kaplan-Meier estimation, and any survival analysis workflow."},"files":[{"bytes":15102,"path":"backend/cli/skills/biology/scikit-survival/SKILL.md","sha256":"00a768180434a3914cd116c90080356590d56c075dd60445da34f9f60eb20bc7","url":"https://skillfed.io/files/synthetic-sciences/openscience/scikit-survival/504caa18/SKILL.md"}],"id":"synthetic-sciences/openscience/scikit-survival","links":{"html":"https://skillfed.io/synthetic-sciences/openscience/scikit-survival","md":"https://skillfed.io/synthetic-sciences/openscience/scikit-survival.md","repo":"https://github.com/synthetic-sciences/openscience"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":403,"language":"TypeScript","last_updated":"2026-07-27","license":"Apache-2.0","name":"scikit-survival","publisher":"synthetic-sciences","stars":2896},"relations":{"similar":[{"id":"foryourhealth111-pixel/Vibe-Skills/scikit-survival"},{"id":"LeonChaoX/qinyan-academic-skills/scikit-survival"},{"id":"drshailesh88/integrated_content_OS/scikit-survival"},{"id":"beita6969/ScienceClaw/scikit-survival"},{"id":"jaechang-hits/SciAgent-Skills/scikit-survival-analysis"},{"id":"K-Dense-AI/scientific-agent-skills/scikit-survival"},{"id":"DrugClaw/DrugClaw/survival-analysis-tools"},{"id":"beita6969/ScienceClaw/biostatistics"},{"id":"aj-geddes/useful-ai-prompts/survival-analysis"},{"id":"tondevrel/scientific-agent-skills/lifelines"}]},"slug":{"owner":"synthetic-sciences","repo":"openscience","skill":"scikit-survival"},"version":"504caa18"}