This repository contains a Jupyter Notebook demonstrating the implementation of Bayesian Hyperparameter Optimization. The notebook offers a comprehensive guide to optimizing machine learning model parameters using Bayesian optimization techniques, focusing on achieving higher performance with fewer iterations compared to traditional grid or random search methods.
##Features Detailed implementation of Bayesian Optimization. Comparison with traditional hyperparameter tuning methods. Visualizations for better understanding of the optimization process. Use case on a sample dataset to demonstrate efficacy.
Before you begin, ensure you have met the following requirements:
Python 3.x Jupyter Notebook or JupyterLab installed Familiarity with machine learning concepts
To use this notebook, follow these steps:
git clone https://github.com/your-username/bayesian-optimization-notebook.git
cd bayesian-optimization-notebook
pip install -r requirements.txt
If you want to contact me, you can reach me at email@maikpaixao.com.br
This project uses the following license: MIT License.