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scikit-survival

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About scikit-survival

S

scikit-survival

Builds, evaluates

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.

View scikit-survivalAll AI Skills apps
Price
Free
Made by
k-dense-ai
Category
AI Skills
Website
github.com/k-dense-ai/scientific-agent-skills/scikit-survival