fabsig/GPBoost
Tree-Boosting, Gaussian Processes, and Mixed-Effects Models
This tool helps data scientists and analysts build more accurate predictive models, especially when working with complex datasets like panel data, spatial data, or data with many categorical variables. It takes your raw data, applies advanced statistical modeling techniques like tree-boosting and mixed-effects models, and outputs a highly predictive model for forecasting or understanding relationships.
665 stars. Used by 1 other package. Actively maintained with 5 commits in the last 30 days. Available on PyPI.
Use this if you need to predict outcomes more accurately by modeling both fixed (e.g., demographics) and random effects (e.g., groups or spatial location) in your data, or when dealing with high-cardinality categorical features.
Not ideal if your primary goal is simple, interpretable linear models without the need for complex non-linear relationships or handling dependent data structures.
Stars
665
Forks
53
Language
C++
License
—
Category
Last pushed
Mar 09, 2026
Commits (30d)
5
Dependencies
6
Reverse dependents
1
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