gbm
There are 132 repositories under gbm topic.
dmlc/xgboost
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
microsoft/LightGBM
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/catboost
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.
h2oai/h2o-3
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.
serengil/chefboost
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
glmark2/glmark2
glmark2 is an OpenGL 2.0 and ES 2.0 benchmark
kanyun-inc/ytk-learn
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).
DataCanvasIO/HyperGBM
A full pipeline AutoML tool for tabular data
szilard/GBM-perf
Performance of various open source GBM implementations
vkmark/vkmark
Vulkan benchmark
graysky2/kodi-standalone-service
Use systemd to allow for standalone operation of kodi.
aws/sagemaker-xgboost-container
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.
feedzai/fairgbm
Train Gradient Boosting models that are both high-performance *and* Fair!
asafschers/scoruby
Ruby Scoring API for PMML
chenhongge/RobustTrees
[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
serengil/decision-trees-for-ml
Building Decision Trees From Scratch In Python
fabsig/KTBoost
A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.
Azure/fast_retraining
Show how to perform fast retraining with LightGBM in different business cases
avafinger/nanopi-m4-ubuntu-base-minimal
Nanopi M4 RK3399 base minimal image for development (mali fbdev / gbm) - Camera support
adamlilith/enmSdm
Faster, better, smarter ecological niche modeling and species distribution modeling
JohnNay/forecastVeg
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python
Allardvm/LightGBM.jl
LightGBM.jl provides a high-performance Julia interface for Microsoft's LightGBM.
YC-Coder-Chen/Tree-Math
Math behind all the mainstream tree-based machine learning models
rishiraj/autolgbm
LightGBM + Optuna: Auto train LightGBM directly from CSV files, Auto tune them using Optuna, Auto serve best model using FastAPI. Inspired by Abhishek Thakur's AutoXGB.
liupei101/Tutorial-Machine-Learning-Based-Survival-Analysis
This repository is a tutorial about survival analysis based on advanced machine learning methods including Random Forest, Gradient Boosting Tree and XGBoost. All of them are implemented in R.
njtierney/broomstick
:evergreen_tree: broom helpers for decision tree methods (rpart, randomForest, and more!) :evergreen_tree:
bottama/stochastic-asset-pricing-in-continuous-time
Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method
haghish/mlim
mlim: single and multiple imputation with automated machine learning
nanxstats/stackgbm
🌳 Stacked Gradient Boosting Machines
chenhongge/treeVerification
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
szilard/GBM-tune
Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions
caramel2001/Financial-Derivative-Analysis-and-Simulation
Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Geometric Brownian Motion, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction.(For MH4518 Project)
jd-opensource/UTBoost
A powerful tree-based uplift modeling system.
pierrenodet/spark-ensemble
Ensemble Learning for Apache Spark 🌲