Popular GitHub Repositories for Boosted

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

A curated list of gradient boosting research papers with implementations.

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

Neural Network-Boosted Importance Nested Sampling for Bayesian Statistics

Represent trained machine learning models as Pyomo optimization formulations

LightNet++: Boosted Light-weighted Networks for Real-time Semantic Segmentation

This project studies the intrinsic relationship between the stocks’ multiple factors and the investment value of the stocks listed in China Securities Index 800 Index through the machine method. The investment system pipeline has been implemented including data acquirement, data preprocessing, model tuning and selection based on the XGBoost boosted tree model.

Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark

A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)

A lightweight gradient boosted decision tree package.

Library for Multi-objective optimization in Gradient Boosted Trees

Federated gradient boosted decision tree learning

Multiobjective black-box optimization using gradient-boosted trees

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

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

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

Machine Learning Tool Box

This repository provides an example of dataset preprocessing, GBRT (Gradient Boosted Regression Tree) model training and evaluation, model tuning and finally model serving (REST API) in a containerized environment using MLflow tracking, projects and models modules.

A machine learning boosted parallel-in-time differential equation solver framework.