This is probably the most easily understood type of model aggregation. It just means the final output will be the majority or average of prediction output values from multiple models. It's also possible to assign different weights to each model in the ensemble, for example, some models might consider two votes. However, combining the results of models that are highly correlated to each other doesn't guarantee spectacular improvements. It's better to somehow diversify the models by using different features or different algorithms. If we find that two models are strongly correlated, we may, for example, decide to remove one of them from the ensemble and increase proportionally the weight of the other model.