Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Lightgbm | 16,056 | 278 | 574 | 13 days ago | 34 | September 12, 2023 | 345 | mit | C++ | |
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. | ||||||||||
Catboost | 7,564 | 12 | 4 months ago | 20 | September 19, 2023 | 539 | apache-2.0 | Python | ||
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. | ||||||||||
Chefboost | 428 | 4 months ago | 17 | February 16, 2022 | mit | Python | ||||
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python | ||||||||||
Rgf | 371 | 2 | 8 | 2 years ago | 28 | January 07, 2022 | 6 | C++ | ||
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers. | ||||||||||
30 Days Of Ml Kaggle | 93 | 3 years ago | mit | Jupyter Notebook | ||||||
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning. | ||||||||||
In Depth Ml | 9 | 5 years ago | mit | Jupyter Notebook | ||||||
In depth machine learning resources | ||||||||||
Autolgbm | 7 | 2 years ago | 3 | February 13, 2022 | apache-2.0 | Python | ||||
LightGBM + Optuna | ||||||||||
Mobile Phones Price Prediction | 5 | 6 years ago | Jupyter Notebook | |||||||
A set of different models, that can be used to predict price range of a mobile phone. |