or how fast each tree should learn the error of the previous tree. Here we also introduce:  
- `caret::trainControl` for defining cross-validation (rather than bootstrap) settings  
- `caret::expand.grid` for creating a grid search of hyperparameters to tune, allowing us to fit multiple combinations of the algoritihm simultaneously. 
- *A*rea *U*nder the Receiver Operating Characteristic *C*urve (AUC) as a performance metric.  

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# XGBoost

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.center[![](slides_img/xgb-cvhyper.png)]
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