Question

8.5 In order to perform Boosting, we need to select 3 parameters: number of samples B, tree depth d, and step size . How many parameters do we need to select in order to perform Random Forests?:

Answer 1

The 2 parameters that can be selected in order to perform Random Forests are:

1. Number of trees - the bigger, the better. You almost can't overshoot with this parameter, but of course the upper limit depends on the computational time you want to spend on RF.
The good idea is to make a long forest first and then see (I hope it is available in MATLAB implementation) when the OOB accuracy converges.

2. Number of tried attributes the default is square root of the whole number of attributes, yet usually the forest is not very sensitive about the value of this parameter -- in fact it is rarely optimized, especially because stochastic aspect of RF may introduce larger variations.