skbayes
Bayesian Machine Learning with scikit-learn API
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is skbayes?
Python package for Bayesian Machine Learning that integrates seamlessly with the popular scikit-learn framework, offering a familiar interface while enabling probabilistic modeling.
Key differentiator
“skbayes stands out by providing Bayesian machine learning capabilities within the well-known scikit-learn framework, making it accessible to a wide range of users without sacrificing advanced statistical methods.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Only supports Bayesian linear regression and Gaussian processes, lacking more advanced models like hierarchical Bayesian modeling.
Gaussian process computations can be computationally expensive, leading to slow training times for larger datasets.
GitHub activity is low with few contributors and limited user support available through forums or documentation.
Fit analysis
Who is it for?
✓ Best for
Teams looking to integrate Bayesian methods into their ML pipelines using familiar APIs
Developers who need probabilistic predictions for decision-making processes
✕ Not a fit for
Projects requiring real-time inference with minimal latency
Applications where deterministic models are preferred over probabilistic ones
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
Works well with
Next step
Get Started with skbayes
Step-by-step setup guide with code examples and common gotchas.