xgboost-genetic 0.0.2

Biblioteca para otimização de hiperparâmetros do XGBoost usando algoritmos genéticos.

XGBoost Genetic Algorithm 🧬 + 📊

This project combines the power of XGBoost and Genetic Algorithms to optimize hyperparameters for machine learning models. By using a genetic approach, we can efficiently search for the best-performing set of hyperparameters for XGBoost, improving model performance and reducing manual hyperparameter tuning time.

Table of Contents 📑

• Introduction
• Features
• Getting Started
• Installation
• Usage
• Example
• Contributing
• License

Introduction 🌟

Hyperparameter tuning is often a time-consuming task in machine learning. While techniques like grid search or random search are common, they can be computationally expensive. This repository introduces a novel approach using genetic algorithms to optimize XGBoost hyperparameters. The algorithm mimics the process of natural selection to find the best hyperparameters more efficiently.

By evolving a population of candidate solutions, this method automatically adjusts the parameters to improve the XGBoost model's accuracy, speed, and overall performance.

Features 🚀

• Genetic Algorithm to optimize hyperparameters for XGBoost.
• Efficient search for optimal configurations using crossover, mutation, and selection.
• Easily extensible and configurable for different datasets and tasks.
• Compatible with any dataset where XGBoost is applicable.

Getting Started 🛠️

To get started with xgboost_genetic, follow the instructions below to install and run the project.

Prerequisites

Make sure you have the following installed:

• Python 3.6+
• pip (Python package installer)

Installation 📦

1. Install from PyPi:

   pip3 install xgboost_genetic
   

Usage 🧑‍💻

Once installed, you can run the genetic algorithm for hyperparameter optimization. Here's an example usage:

Example 📈

Here's an example of how the algorithm can optimize the hyperparameters for an XGBoost model on a classification task:

Contributing 💡

We welcome contributions to make this project even better!

• Fork the repo and create your branch.
• Submit a pull request with your changes.
• Follow the coding conventions and add tests if possible.

If you have any ideas or suggestions, feel free to open an issue.

License 📄

This project is licensed under the MIT License - see the LICENSE file for details.