Scikit-Survival
Survival analysis built on top of scikit-learn for Python.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Scikit-Survival?
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.
Key differentiator
“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.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Scikit-Survival primarily supports a subset of models like Cox proportional hazards, lacking some advanced or niche models available in other packages.
The library may struggle to process very large datasets efficiently due to its reliance on Python and NumPy for core computations.
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.
Fit analysis
Who is it for?
✓ Best for
Researchers needing to perform survival analysis within a familiar scikit-learn framework
Projects requiring integration with existing scikit-learn workflows for preprocessing and validation
✕ Not a fit for
Developers looking for a cloud-based service for survival analysis
Teams preferring proprietary software over open-source solutions
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with Scikit-Survival
Step-by-step setup guide with code examples and common gotchas.