Would you recommend feature normalization when using boosting trees? For some machine learning methods it is recommended to use feature normalization to use features that are on the same scale, especially for distance based methods like k-means or when using regularization. However, in my experience, boosting tree regression works less well when I use normalized features, for some strange reason. How is your experience using feature normalization with boosted trees does it in general improve our models?
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