Since random forest includes a bunch of random decision trees, it is not clear when we say forest size, it can be :
1) number of bits it takes
2) number of decision trees included in forest
3) accuracy
4) execution time
5) a manifold of all of above terms
6) etc.
What is the most important and what is the best?
To answer a question about most important and/or best one needs to know the goal. For example, if I was going to use a random forest in an adaptive controller with 512 bytes of RAM, representation bits would be a very important design constraint. If my random forest were to be embedded into a real-time system, execution time would be key. Medical diagnosis would usually maximize some sort of risk-weighted accuracy and sacrifice other criteria.
To summarize: depending on the application different measures will be important.
1. and 2. Information and number of decisions (competing and/or uncorrelated decision points).
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