sebp/scikit-survival

Health v2 · maintenance only

Adoption not part of the score

1320 stars · 231 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

scikit-survival is a Python library for survival analysis (time-to-event modeling) built on top of scikit-learn. It provides survival models that integrate with scikit-learn's pipeline, preprocessing, and cross-validation utilities while properly handling censored observations.

Use cases

• predict time-to-event outcomes with censored data
• fit survival models in a scikit-learn pipeline
• run cross-validation on survival analysis datasets
• analyze clinical study data with right-censored records
• estimate survival curves from covariates
• do reliability analysis in Python

When to choose

• you need survival analysis compatible with scikit-learn workflows
• your training data contains censored observations
• you want random forests, gradient boosting, or linear models for time-to-event prediction

When to avoid

• your data has no censoring and standard regression suffices
• you need deep-learning-based survival models
• you work outside the Python/scikit-learn ecosystem

Facets

library · maturity active

machine-learning data-science machine-learning data-science healthcare python survival-analysis scikit-learn time-to-event censored-data statistics

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For agents

markdown · JSON · MCP: product_card(name="sebp/scikit-survival")