# sebp/scikit-survival

Survival analysis built on top of scikit-learn

Repository: https://github.com/sebp/scikit-survival
Canonical: https://ross.abutalabs.com/products/scikit-survival
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: survival-analysis, machine-learning, python, scikit-learn
Last push: 2026-08-23T05:49:19+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 68, longevity 100
- inputs: {"age_days": 3537, "days_push": 10, "days_rel": 59, "gap_med": 114.5, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1320, forks 231 (observed 2026-08-28T04:04:21.691638+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: machine-learning, data-science
- domain: machine-learning, data-science, healthcare
- platform: python
- tags: survival-analysis, scikit-learn, time-to-event, censored-data, statistics

## Member repositories
- sebp/scikit-survival (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:21.691638+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:47:39.213790+00:00, confidence not recorded.
  - readme: https://github.com/sebp/scikit-survival (fetched 2026-08-28T04:04:21.691638+00:00, sha b8cf512ad1ef)
  - registry_pypi: https://pypi.org/pypi/scikit-survival/json (fetched 2026-08-29T12:06:28.364216+00:00, sha e4e7b860b2ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.