scikit-survival
Scope
Use this skill for scikit-survival 0.28.0 workflows involving:
- right-censored structured outcomes;
- Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs;
- discrimination, prediction error, calibration-oriented checks, and time-dependent prediction;
- nonparametric cumulative incidence with competing risks;
- scikit-learn pipelines, nested model selection, and reproducible reports.
scikit-survival primarily models right-censored outcomes. Its built-in competing-risk support is nonparametric cumulative incidence; it does not provide Fine-Gray regression. Do not present model output as clinical advice, causal evidence, or proof of clinical utility.
Current release and installation
Verified 2026-07-23:
- Latest stable: scikit-survival 0.28.0, released 2026-07-05.
- Python: 3.11 or later; PyPI wheels cover CPython 3.11-3.14 on Linux x86-64, macOS x86-64/ARM64, and Windows x86-64.
- Runtime bounds: NumPy >=2.0.0, pandas >=2.2.0, SciPy >=1.13.0, scikit-learn >=1.9.0,<1.10, OSQP >=1.0.2, narwhals >=2.0.1.
- 0.28 adds pandas/Polars estimator support through narwhals and removes
criterionfrom `GradientBoostingSurviva…
