I know that the random forest algorithm works by generating a set of decision trees with a subset of features.
Say I was using random forest as a classification algorithm looking at someone's data usage out of a data allowance.
One decision within one of the trees may be if data_usage > 500 branch right else branch left.
My question is: does a decision tree ever make a decision based on two variables at one split?
For example if data_usage > data_allowance branch right else branch left. Or would it be better to encode a new feature data_utilisation as a % usage of their data allowance? So if data_usage > 100 branch right else branch left?