Fit survival models and evaluate censored time-to-event data in Python.
npx skills add https://github.com/jacketlong23/skills --skill scikit-survival-jacketlong23
Please help me install this Agent Skill. Skill: scikit-survival Source: https://github.com/jacketlong23/skills/tree/main/scikit-survival Command: npx skills add https://github.com/jacketlong23/skills --skill scikit-survival-jacketlong23
This Skill provides a comprehensive toolkit for survival analysis and time-to-event modeling in Python, enabling users to analyze censored data and build predictive models for time-based outcomes.
Use the scikit-survival skill to fit a Cox proportional hazards model to the provided dataset.
High-intent search queries and answers about installing and using this skill.
Perform survival analysis on censored data in Python by fitting models like Cox proportional hazards or Random Survival Forests to time-to-event datasets, enabling predictive modeling for outcomes where the event has not yet occurred for all subjects.
Evaluate time-to-event model performance using metrics like the concordance index, time-dependent AUC, and Brier score to measure predictive accuracy and calibration for survival models handling censored observations.
Random Survival Forests and Gradient Boosting can be used for predictive modeling of time-to-event data, offering robust methods to handle censored observations and predict disease recurrence or failure times effectively.
Cox models, including CoxPH and CoxNet, are fully supported for time-to-event analysis, allowing you to fit proportional hazards models to survival datasets and handle censored data efficiently.
Handle censored data when predicting disease recurrence by creating structured survival outcomes using provided utilities, fitting appropriate survival models, and evaluating predictions with metrics designed for incomplete time-to-event observations.
Survival SVMs for time-to-event modeling may face limitations with very large datasets or complex censoring patterns, requiring careful preprocessing and validation to ensure reliable predictive performance compared to tree-based survival methods.
HaykTarkhanyanFit scikit-survival Cox and ensemble models on censored time-to-event data.
LogauaEngstromFit survival models and evaluate censored time-to-event data with scikit-survival.
xjtulycFit Kaplan-Meier, Cox, and AFT models to right-censored time-to-event data.
shushuznBuild censored-data survival models with scikit-survival for Cox, ensemble, and SVM approaches.
