gbm

There are 144 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

    Language:C++26.4k9085.3k8.7k
  • LightGBM

    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.

    Language:C++16.8k4353.4k3.8k

  • 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.

    Language:Python8.2k1912.3k1.2k

  • 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.

    Language:Jupyter Notebook7k3869.5k2k

  • 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

    Language:Python4631850101

  • glmark2/glmark2

    glmark2 is an OpenGL 2.0 and ES 2.0 benchmark

    Language:C43926127182

  • 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).

    Language:Java348361276

  • DataCanvasIO/HyperGBM

    A full pipeline AutoML tool for tabular data

    Language:Python343165546

  • perpetual-ml/perpetual

    A self-generalizing gradient boosting machine which doesn't need hyperparameter optimization

    Language:Rust32971712

  • szilard/GBM-perf

    Performance of various open source GBM implementations

    Language:HTML216225528

  • vkmark/vkmark

    Vulkan benchmark

    Language:C++185153931

  • graysky2/kodi-standalone-service

    Use systemd to allow for standalone operation of kodi.

    Language:Roff16884237

  • 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.

    Language:Python12829079

  • feedzai/fairgbm

    Train Gradient Boosting models that are both high-performance *and* Fair!

    Language:C++10313325

  • qiyiping/gbdt

    Language:C++9312569

  • asafschers/scoruby

    Ruby Scoring API for PMML

    Language:Ruby683812

  • chenhongge/RobustTrees

    [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

    Language:C++677311

  • serengil/decision-trees-for-ml

    Building Decision Trees From Scratch In Python

    Language:Jupyter Notebook659151

  • fabsig/KTBoost

    A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

    Language:Python6361119

  • Azure/fast_retraining

    Show how to perform fast retraining with LightGBM in different business cases

    Language:Jupyter Notebook54214415

  • avafinger/nanopi-m4-ubuntu-base-minimal

    Nanopi M4 RK3399 base minimal image for development (mali fbdev / gbm) - Camera support

    5353310
  • enmSdm

    adamlilith/enmSdm

    Faster, better, smarter ecological niche modeling and species distribution modeling

    Language:R4991320

  • JohnNay/forecastVeg

    A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

    Language:Python478120

  • Allardvm/LightGBM.jl

    LightGBM.jl provides a high-performance Julia interface for Microsoft's LightGBM.

    Language:Julia40179

  • YC-Coder-Chen/Tree-Math

    Math behind all the mainstream tree-based machine learning models

    35204

  • bottama/stochastic-asset-pricing-in-continuous-time

    Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method

    Language:Python342010

  • 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.

    Language:Python34125

  • 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.

    Language:Jupyter Notebook312113

  • haghish/mlim

    mlim: single and multiple imputation with automated machine learning

    Language:R30211

  • njtierney/broomstick

    :evergreen_tree: broom helpers for decision tree methods (rpart, randomForest, and more!) :evergreen_tree:

    Language:R2810141

  • jd-opensource/UTBoost

    A powerful tree-based uplift modeling system.

    Language:C++27302

  • 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)

    Language:Jupyter Notebook26106

  • 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)

    Language:C++26216
  • stackgbm

    nanxstats/stackgbm

    🌳 Stacked Gradient Boosting Machines

    Language:R253131
  • spark-ensemble

    pierrenodet/spark-ensemble

    Ensemble Learning for Apache Spark 🌲

    Language:Scala233177

  • Rpita623/Detecting-Credit-Card-Fraud

    Using R and machine learning to build a classifier that can detect credit card fraudulent transactions.

    Language:R22219