Does the performance of GBM methods profit from feature scaling?
Scaling doesn't affect the performance of any tree-based method, not for lightgbm, xgboost, catboost or even a decision tree.
This post that elaborates on the topic, but mainly the issue is that decision trees split the feature space based on binary decisions like &quot;is this feature bigger than this value?&quot;, and if you scale your data, the decisions might look different, as they are done on the scaled space, but the results should be the same.
As an example, a decision tree should split in the same way the data if you change units (a particular case of scaling). Let's say you want to use weight of a person to predict if someone is under 18 (as a binary classifier). If given the weight in grams, the decision tree might do something like: if weight &lt; 5000 grams, then the person is under 18. If you change the units to kilograms, the decision tree will do: if weight &lt; 5 kg, then the person is under 18.
To sum up, the splits will be equivalent under any scaling or, in general, any increasing linear transformation.