Which between random forest or extra tree is best in a unbalance dataset?
Both Random Forest Classifier and Extra Trees randomly sample the features at each split point, but because Random Forest is greedy it will try to find the optimal split point at each node whereas Extra trees selects the split point randomly.
I would choose Random Forest because it's more likely to create a split point that accounts for the imbalanced class, whereas Extra Trees might keep splitting over and over again on a subset of the data without separating out class 3 due to the random split point.