Is it important to scale all the features into a common range (normalized) when using random forests (bagging) in classification. Or can random forests handle features in different ranges without problems (bias to the larger values). Some features may have a value in the 1000-range and others in the 0-1 range.
Partially answered in comments:
There are similar questions on StackOverflow (https://stackoverflow.com/questions/8961586/do-i-need-to-normalize-or-scale-data-for-randomforest-r-package) and Quora (https://www.quora.com/Machine-Learning/Should-inputs-to-random-forests-be-normalized?srid=3EJy&st=ns). The short answer is that you don't. – tchakravarty