There are 3 tuning parameters when using Boosting:

1. The first is the number of trees, denoted by $B$. Cross-validation is used in order to select $B$. If the value of $B$ is too big, it may overfit the data, albeit slowly.
2. The second tuning parameter is the shrinkage parameter ($\lambda$), which controls the rate at which the boosting learns. This value is typically a small positive value; if it is very small, a large $B$ value may be required for it to work well.
3. The third tuning parameter is $d$, which is the number of splits in each tree. This value controls how complex the boosting will be. If $d = 1$, there is a single split, where each model has at most 1 variable (also known as a stump). It is also known as the ‘interaction depth’, which controls the interaction order.