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
-
Updated
Sep 1, 2026 - C++
#
gbm
Here are 208 public repositories matching this topic...
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.
python machine-learning data-mining r parallel distributed kaggle gbdt gbm lightgbm gbrt decision-trees gradient-boosting
-
Updated
Sep 1, 2026 - C++
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.
python data-science machine-learning data-mining tutorial r big-data gpu cuda kaggle gbdt gbm gpu-computing decision-trees gradient-boosting coreml catboost categorical-features
-
Updated
Sep 1, 2026 - C++
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.
python java data-science machine-learning opensource r big-data spark deep-learning hadoop random-forest gpu naive-bayes h2o distributed pca gbm ensemble-learning automl h2o-automl
-
Updated
Sep 1, 2026 - Jupyter Notebook
Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R
python rust data-science machine-learning kaggle gbdt gbm gradient-boosting gradient-boosted-trees gradient-boosting-decision-trees
-
Updated
Apr 2, 2026 - Rust
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
python data-science machine-learning data-mining random-forest kaggle id3 gbdt gbm gbrt gradient-boosting-machine cart adaboost decision-trees gradient-boosting c45-trees categorical-features gradient-boosting-machines regression-tree
-
Updated
May 13, 2026 - Python
A full pipeline AutoML tool for tabular data
sklearn tabular-data xgboost semi-supervised-learning gpu-acceleration gbm lightgbm ensemble-learning dask preprocessing automl distributed-training datacleaning catboost pseudo-labeling dask-distributed rapidsai fullpipeline adversarial-validation
-
Updated
Apr 20, 2026 - Python
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).
-
Updated
Jul 6, 2022 - Java
Performance of various open source GBM implementations
-
Updated
Feb 17, 2026 - HTML
Use systemd to allow for standalone operation of kodi.
-
Updated
Jun 5, 2026 - Roff
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.
-
Updated
Jul 1, 2026 - Python
Train Gradient Boosting models that are both high-performance *and* Fair!
-
Updated
Aug 31, 2026 - C++
machine-learning gbdt gbm gbrt gradient-boosting-machine boosting-algorithms gradient-boosting gradient-boosting-decision-trees
-
Updated
Jun 19, 2022 - C++
PKBoost: Adaptive GBDT for Concept Drift, Built from scratch in Rust, PKBoost manages changing data distributions in fraud detection with a fraud rate of 0.2%. It shows less than 2% degradation under drift. In comparison, XGBoost experiences a 31.8% drop and LightGBM a 42.5% drop
rust machine-learning classification gbdt gbm gradient-boosting-machine imbalanced-data automl fraud-detection gradient-boosting gradient-boosting-decision-trees data-drift
-
Updated
Jul 16, 2026 - Rust
Ruby Scoring API for PMML
ruby ruby-gem machine-learning random-forest naive-bayes classification gbm pmml decision-tree gradient-boosting-classifier rubyml gradient-boosted-models
-
Updated
Oct 19, 2022 - Ruby
Building Decision Trees From Scratch In Python
machine-learning random-forest xgboost id3 gbm lightgbm gradient-boosting-machine cart adaboost c45 decision-tree gradient-boosting boosting bagging regression-trees
-
Updated
Jul 28, 2025 - Jupyter Notebook
[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
-
Updated
Jul 12, 2025 - C++
A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.
-
Updated
Nov 22, 2021 - Python
Add this topic to your repo
To associate your repository with the gbm topic, visit your repo's landing page and select "manage topics."