{"enrichment":{"faq":[{"a":"scikit-survival 0.28.0 enables you to build and evaluate right-censored survival models using Cox proportional hazards, random survival forests, gradient boosting survival analysis, and Cox SVM. Each approach handles censoring natively and integrates with scikit-learn pipelines for leakage-safe preprocessing and nested model selection workflows.","q":"What survival analysis models does scikit-survival support?"},{"a":"scikit-survival enforces leakage-safe data splits and preprocessing pipelines to prevent information from the test set influencing training. Use nested cross-validation for hyperparameter tuning and ensure all transformations\u2014imputation, scaling, feature selection\u2014are fit only on training folds before applying to held-out data.","q":"How do you prevent data leakage in survival preprocessing?"},{"a":"scikit-survival 0.28.0 computes discrimination metrics like IPCW concordance index and time-dependent AUC, calibration assessments, and prediction error measures such as Brier score. These metrics account for right censoring and competing risks, enabling rigorous model comparison on survival outcomes.","q":"What censoring-aware evaluation metrics does scikit-survival compute?"},{"a":"scikit-survival handles competing risks by computing cumulative incidence functions for each event type nonparametrically. This allows you to estimate the probability of a specific event occurring before competing events, essential for realistic risk prediction in multi-event survival settings.","q":"How do you handle competing risks and estimate cumulative incidence?"},{"a":"Yes. scikit-survival models follow scikit-learn's estimator interface and work seamlessly in Pipeline objects. This integration enables you to chain preprocessing, feature engineering, and survival model fitting while maintaining leakage-free cross-validation and hyperparameter tuning.","q":"Can scikit-survival integrate with scikit-learn pipelines for survival prediction?"},{"a":"scikit-survival 0.28.0 includes local CLI tools for survival data validation and reporting, supporting automated workflows for preprocessing checks, model evaluation, and result generation outside interactive notebooks.","q":"Does scikit-survival offer command-line tools for survival workflows?"}],"shadow_tags":["time-to-event-modeling","censored-outcomes","hazard-regression","risk-stratification","competing-events","reproducible-ml-pipelines","medical-statistics","survival-metrics","feature-leakage-prevention","ensemble-survival-methods"],"summary_rewrite":"scikit-survival 0.28.0 enables you to construct, tune, and assess survival workflows for right-censored outcomes using Cox models, ensemble methods, and kernel-based approaches. The skill enforces leakage-safe data splits, preprocessing pipelines, and proper metric selection\u2014discrimination, calibration, and prediction error\u2014while handling competing risks through nonparametric cumulative incidence."},"files":[{"bytes":13607,"path":"skills/scikit-survival/SKILL.md","sha256":"7ba791d64e2fb261c6891c0e42d93a0f10db43c4478e8b235961e8f537fb653a","url":"https://skillfed.io/files/K-Dense-AI/scientific-agent-skills/scikit-survival/a258d1be/SKILL.md"}],"id":"K-Dense-AI/scientific-agent-skills/scikit-survival","links":{"html":"https://skillfed.io/K-Dense-AI/scientific-agent-skills/scikit-survival","md":"https://skillfed.io/K-Dense-AI/scientific-agent-skills/scikit-survival.md","repo":"https://github.com/K-Dense-AI/scientific-agent-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":3173,"language":"Python","last_updated":"2026-07-28","license":"MIT","name":"scikit-survival","publisher":"K-Dense-AI","stars":31940},"relations":{"similar":[{"id":"jaechang-hits/SciAgent-Skills/scikit-survival-analysis"},{"id":"synthetic-sciences/openscience/scikit-survival"},{"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":"beita6969/ScienceClaw/biostatistics"},{"id":"tondevrel/scientific-agent-skills/lifelines"},{"id":"aj-geddes/useful-ai-prompts/survival-analysis"},{"id":"aj-geddes/useful-ai-prompts/regression-modeling"}]},"slug":{"owner":"K-Dense-AI","repo":"scientific-agent-skills","skill":"scikit-survival"},"version":"a258d1be"}