I understood that Random Forest and Extremely Randomized Trees differ in the sense that the splits of the trees in the Random Forest are deterministic whereas they are random in the case of an Extremely Randomized Trees (to be more accurate, the next split is the best split among random uniform splits in the selected variables for the current tree). But I don't fully understand the impact of this different splits in various situations.
- How do they compare in terms of bias/variance ?
- How do they compare in presence of irrelevant variables ?
- How do they compare in presence of correlated variables ?