Tuning XGBoost Using Tidymodels

Explore the process of tuning hyperparameters for an XGBoost model using tidymodels and #TidyTuesday data on beach volleyball matches. Dive into data reshaping, gameplay statistics analysis, and error handling. Learn to set up and rename variables, create model specifications, and implement a comprehensive tuning process. Visualize results, reshape data for plotting, and examine variable importance. Conclude with a final model fit and gain insights into optimizing XGBoost performance for predictive modeling in sports analytics.

Introduction
Data
Data reshaping
Gameplay stats
Errors
Setup
Rename with
Exploring
Model specification
Tuning
Finalize
Preprocessor
Tuning process
Visualization
Reshape
Plot
Variable importance
Last fit
Conclusion