This project provides a concise template for integrating XGBoost, a powerful machine learning library for gradient boosting, with Optuna, an advanced hyperparameter optimization framework. The goal is to leverage Optuna's efficient search capabilities for fine-tuning XGBoost models.
- XGBoost: Utilizes XGBoost for building robust and fast gradient boosting models, suitable for various data science tasks.
- Optuna Integration: Implements Optuna for deep grid search, enabling efficient and automated hyperparameter tuning.
- Optimization Strategy: Employs Optuna's advanced algorithms for searching the optimal set of hyperparameters in XGBoost models.
- Customizable Search Space: Allows users to define a broad or narrow search space for hyperparameters, accommodating different levels of optimization depth.
- Performance Metrics: Includes metrics for evaluating model performance, making it easier to compare different hyperparameter configurations.
- Scalability: Designed to scale seamlessly with increasing data sizes and complexity.
- Prerequisites: Ensure Python, XGBoost, and Optuna are installed.
- Configuration: Set up the configuration file with the desired search space for hyperparameters.
- Running the Optimization: Execute the script to start the deep grid search process. The progress and results will be logged for analysis.
This project welcomes contributions. Feel free to fork the repository, make improvements, and submit pull requests.