Consider an alternative way of learning a Random Forest where instead of randomly sampling the attributes at each node, we sample a subset of attributes for each tree and build the tree on these features. Would you prefer this method over the original or not, and why?
Yes, because it reduces the correlation between the resultant trees
Yes, because it reduces the time taken to build the trees due to the decrease in the attributes considered
No, because many of the trees will be bad classifiers due to the absence of critical features considered in the construction of some of the trees
