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

Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R

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.

Build and Tune Several Models

4Geeks Academy data science boot camp tree based methods assignment

microGBT is a minimalistic Gradient Boosting Trees implementation

Regular Python library for the original GBDT-MO algorithm. Gradient Boosting Decision Trees with native multiple outputs.

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

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

Sentiment analysis of IMDB data using regular classifiers and RNN

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

Predictions on the breast cancer data set after feature reduction

🐝 Materials and homework assignments for HSE recommender systems course