Metadata-Version: 2.4
Name: rxgb
Version: 0.1.1
Summary: RXGB: A bagging ensemble of shallow XGBoost learners for classification and regression
Author-email: Kiarash Nouri <kia1391n@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/Kia-ML/rxgb
Keywords: machine learning,ensemble,xgboost,bagging,random forest
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: xgboost>=1.7
Requires-Dist: scikit-learn>=1.0
Requires-Dist: numpy>=1.21
Dynamic: license-file

# RXGB: Random eXtreme Gradient Boosting

A bagging ensemble framework that combines Random Forest's bagging strategy
with depth-constrained XGBoost learners as base estimators.

[![Python](https://img.shields.io/badge/python-3.9%2B-blue)](https://www.python.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Overview

RXGB substitutes conventional decision trees in Random Forest with
depth-constrained XGBoost learners. This combination simultaneously
reduces **bias** and **variance**, which neither standalone Random Forest
nor standalone XGBoost can achieve alone.

## Installation

```bash
pip install rxgb
