Scaling a dataset for Random Forest modelling is not necessary. However, if we have already done the scaling and normalization to the dataset, will it impact our Random Forest modelling?
Any monotonic injective transformation of the features won't change the model wrt how it splits the data. The reason is the same as for why scaling is unnecessary: the random forest looks for partitions, and partitions only depend on how the data are sorted. If there is an optimal split on some scale, then by the definition of monotonic injective, the same split exists after transformation, and it's just as good (at splitting the training data, at least).