A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules
GitHub repository with 5 stars and 2 forks.
Language: C++
Topics: gradient-boosting, machine-learning, multi-target-regression, multilabel-classification, multioutput-regressor, rule-learning, scikit-learn
2026-06-05: 5 stars and 2 forks.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
GitHub repository with 8,972 stars and 1,297 forks.
Trending score: 0.49; stars gained: +2; forks gained: +0.
Language: C++
Topics: machine-learning, decision-trees, gradient-boosting, gbm, gbdt, python
Fit interpretable models. Explain blackbox machine learning.
GitHub repository with 6,867 stars and 784 forks.
Trending score: 0.03; stars gained: +0; forks gained: +0.
Language: C++
Topics: ai, artificial-intelligence, bias, blackbox, differential-privacy, explainability
LLM inference in C/C++
GitHub repository with 114,802 stars and 19,206 forks.
Trending score: 4.40; stars gained: +304; forks gained: +99.
Language: C++
Topics: ggml
DuckDB is an analytical in-process SQL database management system
GitHub repository with 38,625 stars and 3,301 forks.
Trending score: 3.50; stars gained: +40; forks gained: +6.
Language: C++
Topics: analytics, database, embedded-database, olap, sql
Community maintained hardware plugin for vLLM on Ascend
GitHub repository with 2,201 stars and 1,350 forks.
Trending score: 3.25; stars gained: +16; forks gained: +22.
Language: C++
Topics: ascend, inference, llm, llm-serving, llmops, mlops
ClickHouse® is a real-time analytics database management system
GitHub repository with 47,840 stars and 8,471 forks.
Trending score: 2.96; stars gained: +53; forks gained: +10.
Language: C++
Topics: ai, analytics, big-data, clickhouse, cloud-native, cpp
Truly independent web browser
GitHub repository with 63,807 stars and 3,076 forks.
Trending score: 2.89; stars gained: +52; forks gained: +5.
Language: C++
Topics: browser, browser-engine
GitHub repository with 6,792 stars and 5,145 forks.
Trending score: 2.66; stars gained: +20; forks gained: +6.
Language: C++
A game theoretic approach to explain the output of any machine learning model.
GitHub repository with 25,504 stars and 3,726 forks.
Trending score: 0.56; stars gained: +2; forks gained: +0.
Language: Jupyter Notebook
Topics: interpretability, machine-learning, deep-learning, gradient-boosting, shap, shapley
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
GitHub repository with 8,972 stars and 1,297 forks.
Trending score: 0.49; stars gained: +2; forks gained: +0.
Language: C++
Topics: machine-learning, decision-trees, gradient-boosting, gbm, gbdt, python
Fit interpretable models. Explain blackbox machine learning.
GitHub repository with 6,867 stars and 784 forks.
Trending score: 0.03; stars gained: +0; forks gained: +0.
Language: C++
Topics: ai, artificial-intelligence, bias, blackbox, differential-privacy, explainability