GPBoost and awesome-gradient-boosting-papers
These are ecosystem siblings: one is a gradient boosting implementation library that combines trees with Gaussian processes for predictive modeling, while the other is a curated research repository documenting the theoretical foundations and papers that inform such implementations.
Maintenance
16/25
Adoption
20/25
Maturity
25/25
Community
16/25
Maintenance
6/25
Adoption
10/25
Maturity
16/25
Community
23/25
Stars: 665
Forks: 53
Downloads: 5,433
Commits (30d): 5
Language: C++
License: —
Stars: 1,045
Forks: 164
Downloads: —
Commits (30d): 0
Language: Python
License: CC0-1.0
No risk flags
No Package
No Dependents
About GPBoost
fabsig/GPBoost
Tree-Boosting, Gaussian Processes, and Mixed-Effects Models
About awesome-gradient-boosting-papers
benedekrozemberczki/awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
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