Description
scikit-survival is an agent skill for building, evaluating and auditing survival workflows with scikit-survival 0.28.0, for right-censored or competing-risk outcomes. It covers Cox proportional hazards models, Coxnet, IPC ridge regression, survival trees, forests, boosting and support vector machines. It also covers discrimination, prediction error, calibration-oriented checks and time-dependent prediction, along with nonparametric cumulative incidence for competing risks. The workflows use scikit-learn pipelines with leakage-safe preprocessing, nested model selection and reproducible reports. The skill is clear about its limits. The library mainly models right-censored outcomes. Its built-in competing-risk support is nonparametric cumulative incidence, and it does not provide Fine-Gray regression. Model output should not be presented as clinical advice, causal evidence or proof of clinical usefulness. Use it for time-to-event analysis in research, such as survival prediction from patient data, after you have a clear outcome and censoring scheme. It is part of the k-dense-ai scientific agent skills repository on GitHub, which has about 49,600 stars.…
Highlights
- Cox, Coxnet and IPC ridge models
- Survival trees, forests, boosting and SVMs
- Leakage-safe pipelines and nested selection
- Cumulative incidence for competing risks
- Targets scikit-survival 0.28.0
Ratings & Reviews
No ratings yet
Be the first to rate scikit-survival.
People also visit
pytorch-patterns
PyTorch deep learning patterns and best practices for building robust
scikit-survival FAQ
What outcomes does it model?
Mainly right-censored outcomes, with competing risks handled through nonparametric cumulative incidence.
Does it provide Fine-Gray regression?
No. The skill says the library does not provide it.
Can I present the output as clinical advice?
No. The skill says model output must not be presented as clinical advice, causal evidence or proof of clinical utility.
What models are included?
Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting and SVMs.
How do I install it?
Run npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill scikit-survival.
Is it checked for security?
Its security audits show passes from Gen Agent Trust Hub, Socket and Snyk.
What are the best scikit-survival alternatives?
Popular alternatives to scikit-survival include pytorch-patterns, aeon and esm. See all scikit-survival alternatives