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Study mode:
on
1
Introduction
2
Data
3
Data reshaping
4
Gameplay stats
5
Errors
6
Setup
7
Rename with
8
Exploring
9
Model specification
10
Tuning
11
Finalize
12
Preprocessor
13
Tuning process
14
Visualization
15
Reshape
16
Plot
17
Variable importance
18
Last fit
19
Conclusion
Description:
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.

Tuning XGBoost Using Tidymodels

Julia Silge
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