skbayes

What it is

Python package for Bayesian Machine Learning that integrates seamlessly with the popular scikit-learn framework, offering a familiar interface while enabling probabilistic modeling.

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.

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 support compared to full Bayesian frameworks

   Only supports Bayesian linear regression and Gaussian processes, lacking more advanced models like hierarchical Bayesian modeling.

2. 02

   Performance issues with large datasets

   Gaussian process computations can be computationally expensive, leading to slow training times for larger datasets.

3. 03

   Sparse community and limited third-party contributions

   GitHub activity is low with few contributors and limited user support available through forums or documentation.

What it does well

• Seamless integration with scikit-learn API for ease of use

• Supports Bayesian linear regression and Gaussian processes

• Provides probabilistic predictions alongside model parameters

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.