Gbm Perf
Performance of various open source GBM implementations
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Xgboost | 25,253 |  | 796 | 972 | 3 months ago | 79 | November 13, 2023 | 412 | apache-2.0 | C++ |
| Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow | ||||||||||
| Lightgbm | 16,056 |  | 278 | 574 | 4 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 | 3 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. | ||||||||||
| H2o 3 | 6,618 | 62 | 33 | 3 months ago | 49 | August 09, 2023 | 2,746 | apache-2.0 | Jupyter Notebook | |
| H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. | ||||||||||
| Dalex | 1,242 | 18 | 24 | 7 months ago | 29 | January 15, 2023 | 20 | gpl-3.0 | Python | |
| moDel Agnostic Language for Exploration and eXplanation | ||||||||||
| 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 | ||||||||||
| Ytk Learn | 351 | 2 years ago | n,ull | mit | Java | |||||
| Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization Machines, Field-aware Factorization Machines, Logistic Regression, Softmax). | ||||||||||
| Gbm Perf | 180 | 3 years ago | 30 | mit | HTML | |||||
| Performance of various open source GBM implementations | ||||||||||
| Sagemaker Xgboost Container | 109 | 4 months ago | 51 | apache-2.0 | Python | |||||
| This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker. | ||||||||||
| Gbdt | 92 | 2 years ago | 1 | gpl-2.0 | C++ | |||||
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