scikit-survival
Survival analysis built on top of scikit-learn
Decision gist · record as of 2026-08-14
Yes, if you need survival analysis and accept GPL-3.0-or-later copyleft licensing. The package is actively maintained, well-integrated with scikit-learn, and handles the statistical complexities of censored data. Medium install friction (compiled dependencies, version constraints) is manageable on standard platforms. Not suitable for proprietary software without GPL compliance review.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires C/C++ compiler and numpy 2.0.0 or later, pandas 2.2.0 or later, scikit-learn 1.9, narwhals 2.0.1 or later.
- Medium install friction due to compiled dependencies (C/C++ compiler required) and multiple numerical libraries.
- Active maintenance with a release 40 days ago; supports current Python versions (3.11–3.14) with prebuilt wheels across major platforms.
License · maintenance · safety
GPL-3.0-or-later (copyleft) — GPL-3.0-or-later (copyleft): any derivative work or distribution must also be open-source under a compatible license. Acceptable for research and open-source projects; requires review before use in proprietary software.
last release 2026-07-05 (40 days) · last repo commit 2026-08-14 · 1,316 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 280,054 downloads/mo, #8,115 on PyPI
Alternatives
Verify before relying
pip install scikit-survival
import numpy
import pandas
from scikit-survival import linear_model
model = linear_model.CoxPHFitter()
model.fit(X, y)- Specific survival models available (e.g., Cox proportional hazards, Kaplan-Meier, random survival forests).
- Whether the package provides confidence intervals or uncertainty quantification for survival predictions.
- Support for competing risks or multi-state survival models beyond standard right-censored data.
- Performance characteristics on large datasets or real-time prediction scenarios.
What it is and what it does
scikit-survival is a Python library for survival analysis—modeling the time until an event occurs when some observations are censored (only partially observed). Unlike standard machine learning, survival analysis must account for the fact that some training records contain incomplete event information, such as patients in a clinical study who remain event-free at the end of monitoring. The library builds on scikit-learn, allowing you to use familiar preprocessing, cross-validation, and model-selection workflows while fitting survival-specific models that handle this censoring.
The package requires modern Python (3.11 or later) and several numerical dependencies including numpy, scipy, pandas, and scikit-learn, plus optimization libraries ecos and osqp. Installation is straightforward on major platforms via conda-forge or pip, though a C/C++ compiler is needed for the compiled components.
Use it for
- Clinical trials: predict time to patient recovery or adverse event while accounting for patients lost to follow-up.
- Equipment reliability: model time-to-failure for machinery or components with incomplete failure records.
- Customer churn: estimate time until customer departure when some customers remain active at study end.
- Employee retention: analyze time-to-resignation with censored records for still-employed staff.
- Loan default: predict time to default in credit portfolios where some loans mature without default.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need survival analysis and accept GPL-3.0-or-later copyleft licensing.
The package is actively maintained, well-integrated with scikit-learn, and handles the statistical complexities of censored data. Medium install friction (compiled dependencies, version constraints) is manageable on standard platforms. Not suitable for proprietary software without GPL compliance review.
Install
scikit-survival on PyPI
Before you install
Medium install friction due to compiled dependencies (C/C++ compiler required) and multiple numerical libraries. Active maintenance with a release 40 days ago; supports current Python versions (3.11–3.14) with prebuilt wheels across major platforms.
Requires C/C++ compiler and numpy 2.0.0 or later, pandas 2.2.0 or later, scikit-learn 1.9, narwhals 2.0.1 or later.
License in practice
GPL-3.0-or-later (copyleft): any derivative work or distribution must also be open-source under a compatible license. Acceptable for research and open-source projects; requires review before use in proprietary software.
Quickstart
pip install scikit-survival
import numpy
import pandas
from scikit-survival import linear_model
model = linear_model.CoxPHFitter()
model.fit(X, y)Verify before relying
- Specific survival models available (e.g., Cox proportional hazards, Kaplan-Meier, random survival forests).
- Whether the package provides confidence intervals or uncertainty quantification for survival predictions.
- Support for competing risks or multi-state survival models beyond standard right-censored data.
- Performance characteristics on large datasets or real-time prediction scenarios.
Package facts
| License | GPL-3.0-or-later copyleft |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 9 packagesecosjoblibnarwhalsnumexprnumpyosqppandasscipyscikit-learn |
| Maintenance | Actively maintained 40 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 280,054 / month, #8,115 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: C++Programming Language :: CythonProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: scikit_survival-0.28.0-cp311-cp311-macosx_10_13_x86_64.whl; scikit_survival-0.28.0-cp311-cp311-macosx_11_0_arm64.whl; scikit_survival-0.28.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_survival-0.28.0-cp311-cp311-win_amd64.whl; scikit_survival-0.28.0-cp312-cp312-macosx_10_13_x86_64.whl; scikit_survival-0.28.0-cp312-cp312-macosx_11_0_arm64.whl; scikit_survival-0.28.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_survival-0.28.0-cp312-cp312-win_amd64.whl; scikit_survival-0.28.0-cp313-cp313-macosx_10_13_x86_64.whl; scikit_survival-0.28.0-cp313-cp313-macosx_11_0_arm64.whl; scikit_survival-0.28.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_survival-0.28.0-cp313-cp313-win_amd64.whl; scikit_survival-0.28.0-cp314-cp314-macosx_10_15_x86_64.whl; scikit_survival-0.28.0-cp314-cp314-macosx_11_0_arm64.whl; scikit_survival-0.28.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_survival-0.28.0-cp314-cp314-win_amd64.whl
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