Would you recommend feature normalization when using boosting trees?
Boosting trees is about building multiple decision trees. Decision tree doesn't require feature normalization, that's because the model only needs the absolute values for branching.

Wikipedia for decision tree:

Requires little data preparation. Other techniques often require data normalization....

However, it's always a good idea to normalize your features because:


It's easier to visualize and interpret your model
It's easier to compare another model (e.g. SVM) with the same data set