Also note that for ensemble method the trainset is usually splitted into N random subsets when training N models. Thus every model is trained on a different subset (they may overlap). This would lead to the case that every model learns slightly different features, becoming an “expert” on it’s own type of data (they usually tend to overfit). Afterwards the negative results of overfitting in a single model should be canceled out by running the ensemble classifier.