A curated list of gradient boosting research papers with implementations.

python实现GBDT的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision Trees

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

A tree-based federated learning system (MLSys 2023)

This is the official clone for the implementation of the NIPS18 paper Multi-Layered Gradient Boosting Decision Trees (mGBDT) .

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

Assemble an efficient interpretable machine learning workflow.

This repository covers h2o ai based implementations

A machine learning model based on gradient boosting decision tree for predicting heavy metal adsorption in soil.

4Geeks Academy data science boot camp tree based methods assignment

Build and Tune Several Models

microGBT is a minimalistic Gradient Boosting Trees implementation

🔍 Enhance your model's performance with PKBoost, an adaptive gradient boosting library that tackles concept drift in imbalanced multi-class data.

Sentiment analysis of IMDB data using regular classifiers and RNN

This project compares multiple bagging and boosting methods for anomaly detection for the Gecco challenge.

Predictions on the breast cancer data set after feature reduction

🐝 Materials and homework assignments for HSE recommender systems course

Machine Learning | Fall 2023

🪙 Automate cryptocurrency trading with a robust SDK and dynamic grid strategies across major exchanges for effective risk management and real-time execution.