Hands On Guide to XGBoost with Bayesian Optimization
In this comprehensive tutorial, we explore the XGBoost algorithm for machine learning using the heart disease dataset in Visual Studio Code. We delve into setting up the environment, necessary installations, and detailed code walkthroughs. The session covers data preparation, model training, and hyperparameter tuning with Bayesian Optimization, followed by predictions and evaluations. We also compare the differences with the Random Forest model and visualize feature importance using the XGBoost package.
00:00 Introduction to XG Boost
00:33 Setting Up the Environment
01:32 Loading and Preparing the Dataset
03:04 Implementing XG Boost
04:21 Hyperparameter Tuning with Bayesian Optimization
12:59 Evaluating the Model
15:46 Conclusion and Final Thoughts