[D] How to decide number of trees in hyperparameter tuning?

The dataset I have consists of around 2,300 observations and 120 variables, of which around 25 are highly correlated, so I narrowed it down to 95 variables.

I'm using R's boost\_tree() with xgboost as my model.

How do I decide when to stop tuning for number of **trees, mtry, min\_n**, and tree depth, without actually overfitting the data? Because as I increase the number of trees (or any other variable like the ones above), my RMSE obviously goes down, but …

boost dataset how to decide hyperparameter machinelearning trees variables xgboost