Model censored time-to-event data with scikit-survival in Python.
npx skills add https://github.com/Hung-3008/agusta --skill scikit-survival-hung-3008
Please help me install this Agent Skill. Skill: scikit-survival Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/scikit-survival Command: npx skills add https://github.com/Hung-3008/agusta --skill scikit-survival-hung-3008
Comprehensive survival analysis toolkit for Python using scikit-survival, enabling analysts to model censored time-to-event data, compare models, and interpret risk predictions.
Load your dataset, fit a CoxPHSurvivalAnalysis model, and generate risk scores for evaluation.
High-intent search queries and answers about installing and using this skill.
Survival analysis with scikit-survival models censored time-to-event data by integrating directly into scikit-learn pipelines. It supports Cox models, ensemble survival methods, and survival SVMs using NumPy and pandas inputs.
Evaluating survival models across time horizons uses the concordance index, time-dependent AUC, and Brier score. These metrics measure risk prediction accuracy and discriminatory power for censored time-to-event data.
Yes, scikit-survival includes ensemble methods like RandomSurvivalForest and GradientBoostingSurvivalAnalysis, alongside Survival SVMs such as FastKernelSurvivalSVM, to effectively model non-linear relationships in time-to-event data.
Penalized Cox modeling via CoxnetSurvivalAnalysis is needed when handling high-dimensional censored data or preventing overfitting, extending standard CoxPHSurvivalAnalysis by applying regularization to time-to-event risk predictions.
scikit-survival provides references for competing risks support while focusing on standard survival analysis workflows. The toolkit models censored data and generates risk scores using Cox models, survival SVMs, and ensemble methods.
Imad-OuteModel censored time-to-event data with survival analysis in Python.
felixboehmFit Cox models and evaluate survival predictions with scikit-survival.
mkurmanAnalyze censored survival data and build time-to-event models with scikit-survival.
ovachieverPerform survival analysis on censored data with Cox models and ensemble methods.
