Scikit-Survival

What it is

scikit-survival is a Python module that extends the capabilities of scikit-learn to include survival analysis, allowing users to perform advanced statistical analyses while leveraging familiar machine learning workflows and tools.

Scikit-Survival stands out by providing specialized survival analysis capabilities directly within the popular scikit-learn framework, offering seamless integration with existing machine learning pipelines.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited model diversity compared to specialized survival analysis software

   Scikit-Survival primarily supports a subset of models like Cox proportional hazards, lacking some advanced or niche models available in other packages.

2. 02

   Performance issues with large datasets

   The library may struggle to process very large datasets efficiently due to its reliance on Python and NumPy for core computations.

3. 03

   Sparse community support and documentation

   Documentation is concise and lacks comprehensive examples or advanced use cases, while the community size is small, leading to fewer user contributions and slower issue resolution.

What it does well

• Survival analysis built on top of scikit-learn

• Integration with scikit-learn for preprocessing and cross-validation

• Supports various survival models including Cox proportional hazards model

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.