Let's use an example to understand this method of combining models.
Consider that we have run three models and created a table like this:
We have the confidence for each model and its prediction. Let's see how we can combine these models.
If we take a look at the first row, we can see that each of these models is predicting that a person is going to leave. Hence, if we combine the predictions, we are still predicting that the person is going to leave. The confidence value, or the final confidence, is acquired by adding up the confidence values of all the models and dividing by the number of total models, three in our case.
If we look at the second row, we can see that two of these models predict that the person is going to leave; and one model is predicting that the person is going to stay; we can infer that the combined prediction will be that the person will leave. Here, we calculate the confidence values by adding up the confidence of the models that predicted the combined prediction, Leave, divided by the total number of models, which is three. Hence, the final confidence value is low in the second row.
This is combining models by voting, where only the predictions that occur a number of times are considered for combining.