SheffieldML/GPy

Health v2 · maintenance only

Adoption not part of the score

2161 stars · 570 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

GPy is a Python framework for Gaussian process modeling, providing kernels, regression, classification, and sparse GP methods. It is a research-oriented toolbox from the University of Sheffield for probabilistic machine learning with Gaussian processes.

Use cases

• fit a gaussian process regression model in python
• model uncertainty with gaussian processes
• use custom kernels for GP regression
• sparse gaussian process for large datasets
• bayesian optimization surrogate modeling
• GP classification with probabilistic outputs

When to choose

• you need a mature, well-documented Gaussian process library in Python
• you want flexible kernel composition and sparse GP approximations
• you are doing research on GP models and want extensible code

When to avoid

• you need GPU-accelerated or very large-scale GP inference
• you prefer actively developed alternatives like GPyTorch
• you need general-purpose ML beyond Gaussian processes

Facets

library · maturity maintenance

machine-learning data-science machine-learning data-science python cross-platform gaussian-processes kernels regression bayesian-inference probabilistic-modeling algorithms

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