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
Dec 21, 2024 - C++
#
gbm
Here are 144 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.
microsoft python machine-learning data-mining r parallel distributed kaggle gbdt gbm lightgbm gbrt decision-trees gradient-boosting
-
Updated
Dec 21, 2024 - 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
Dec 21, 2024 - Python
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
Dec 21, 2024 - Jupyter Notebook
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
Oct 30, 2024 - 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
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
Jun 23, 2024 - Python
A self-generalizing gradient boosting machine which doesn't need hyperparameter optimization
python rust machine-learning kaggle gbdt gbm gradient-boosting gradient-boosted-trees gradient-boosting-decision-trees
-
Updated
Dec 2, 2024 - Rust
Performance of various open source GBM implementations
-
Updated
Jun 20, 2024 - HTML
Use systemd to allow for standalone operation of kodi.
-
Updated
Jan 1, 2024 - 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
Dec 20, 2024 - Python
Train Gradient Boosting models that are both high-performance *and* Fair!
-
Updated
Jun 21, 2024 - C++
machine-learning gbdt gbm gbrt gradient-boosting-machine boosting-algorithms gradient-boosting gradient-boosting-decision-trees
-
Updated
Jun 19, 2022 - C++
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
[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
-
Updated
Sep 30, 2022 - C++
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
Nov 3, 2019 - Jupyter Notebook
A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.
-
Updated
Nov 22, 2021 - Python
Show how to perform fast retraining with LightGBM in different business cases
distributed-systems benchmark machine-learning azure gpu kaggle xgboost gbdt gbm lightgbm gbrt boosted-trees
-
Updated
Jul 18, 2019 - Jupyter Notebook
Improve this page
Add a description, image, and links to the gbm topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the gbm topic, visit your repo's landing page and select "manage topics."