I actually think that random forests are relatively difficult to tune in comparison to some Boosting methods for example.

The typical parameters are the following: - Number of trees - Percentage of data used to train each tree - Maximal depth of the tree - Minimum information gain (although this can usually be set to 0 and use only the depth) - Minimum sample size (same as the minimal information gain) - Number of thresholds to try for continuous data

Tuning is also difficult because of the non deterministic nature of the algorithm. If you compare to sets of parameters it requires more evaluation in order to be sure if one set is better because of the better choice of the parameters or because the algorithm chose the right data samples. This effect decreases with the number of trees, but the training time is increased in this case.

I think random forests are really good for very large datasets, but in my experience for smaller datasets boosting (for example JointBoost or GentleBoost) can give better results.