Model censored survival data with scikit-survival Cox and random survival forest methods.
npx skills add https://github.com/dralkh/seerai --skill scikit-survival-dralkh
Please help me install this Agent Skill. Skill: scikit-survival Source: https://github.com/dralkh/seerai/tree/main/skills/scikit-survival Command: npx skills add https://github.com/dralkh/seerai --skill scikit-survival-dralkh
This Skill helps you analyze censored time-to-event data with the right survival methods instead of forcing it into ordinary regression or classification workflows.
Use the scikit-survival skill to build a censored survival dataset, fit an appropriate model, and evaluate it with censoring-aware metrics.
High-intent search queries and answers about installing and using this skill.
To model censored time-to-event data, you construct structured survival targets and fit Cox proportional hazards, random survival forests, or survival SVMs. This approach properly accounts for right-censoring rather than forcing incomplete event times into ordinary regression workflows.
Evaluating survival models with censored data requires censoring-aware metrics like Uno's C-index, time-dependent AUC, and Brier score. These metrics measure predictive accuracy correctly by accounting for incomplete observation periods instead of standard classification accuracy.
Yes, you can use scikit-learn pipelines for survival analysis by integrating feature preprocessing, cross-validation, and model comparison. This compatibility allows you to tune penalized Cox models, gradient boosting, and survival SVMs within standard training and validation workflows.
Handling competing risks in biomedical time-to-event datasets involves modeling multiple distinct event types simultaneously. This prevents biased risk estimates by correctly separating the probability of different terminal events rather than treating all outcomes as a single survival curve.
Yes, this survival analysis approach supports random survival forests and gradient boosting for time-to-event outcomes. These tree-based ensemble methods capture non-linear covariate effects and complex interactions that linear Cox proportional hazards models may miss.
jasrajtulsiAnalyze censored survival datasets and estimate time-to-event risk with scikit-survival models.
LeonChaoXBuild censored time-to-event survival models with scikit-survival estimators.
mkurmanAnalyze censored survival data and build time-to-event models with scikit-survival.
yf8578Fit survival models and evaluate censored data with scikit-survival.
