The number of decision trees T in the ensemble is usually chosen to trade off running time against reduced variance. (Typically, a dozen to several thousand trees are used.) The sample size n has a smaller effect on running time, so our choice of n is mainly governed by getting the best predictions. Although it's common practice to set n = n, that isn't necessarily the best choice. How do you recommend we choose the hyperparameter n