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24 alternativesScikit-Survival
Scikit-Survival provides survival analysis built on top of scikit-learn.
About Scikit-Survival
Scikit-Survival is an open source Python module for survival analysis, which studies the time until an event occurs and how covariates relate to that time. It is built on top of scikit-learn, so users can combine survival models with scikit-learn tools such as preprocessing and cross-validation. The module is designed for censored data, where some records are only partly observed, such as patients who remained event-free for the length of a study.
The project requires Python 3.11 or later, numpy 2.0.0 or later, pandas 2.2.0 or later, and scikit-learn 1.9. It also needs a C/C++ compiler. The source recommends installing from conda-forge and lists PyPI and source installation as alternatives. The project is licensed under GPL-3.0.
Key features
- Survival models designed for right-censored data, where the event time is only partly observed
- Integration with scikit-learn tools such as preprocessing and cross-validation
- Installation through conda-forge, PyPI, or from source code
- User guide with an overview of available survival models
- Hands-on examples provided as Jupyter notebooks
Best for: Researchers and data scientists who work in Python, already use scikit-learn, and need to analyse time-to-event data such as clinical study outcomes.
Good for
- Builds on scikit-learn, so existing preprocessing and cross-validation workflows can be reused
- Includes a user guide and Jupyter notebook examples for learning the methods
- Open source and licensed under GPL-3.0
Watch out for
- Requires Python 3.11 or later and specific minimum versions of numpy, pandas, and scikit-learn
- Requires a C/C++ compiler among its listed requirements
- Is a Python module for survival analysis, so it is not a standalone application
Scikit-Survival at a glance
- Category
- Research & Data › Research & Study Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Scikit-Survival FAQ
How can I install scikit-survival?
The easiest method listed is conda install -c conda-forge scikit-survival. It can also be installed from PyPI or from source.
Where should I ask questions about using the library?
Questions on how to use scikit-survival should be posted in GitHub Discussions. General theoretical or methodological questions on survival analysis should go to Cross Validated.
How should I cite scikit-survival?
The project asks users to cite the Journal of Machine Learning Research paper by S. Pölsterl, published in 2020 in volume 21, number 212.
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