Neural Network Multiple | Averging predictions I am training multiple neural networks with various parameters. I am trying to average their predictions, but I am not really sure what that means, I am confused about what to average exactly. Here is what I mean: For a single observation in binary classification for example, the final node will give p a value between 0 and 1 (or -1 and 1 if you're using hyperbolic tangent Activation Function), then this p will be rounded to 1 or 0 if it's > 0.5, depending on your decision boundary.

Now, here is what I don't understand, should average p1, p2 and p3 produced by the models before rounding, or I should round the values to True/False responses and then compute the average? and how does that work exactly ?
There are multiple ways you could do. All of them are categorized under Ensemble methods in machine learning. 

Voting classifiers: which is the simplest way. You just take votes based on the label from all models and uses the majority label. That means, you should first round up all labels to 0 or 1 and then use the majority.

Weighted voting classifiers: similar to previous one, but some models have higher weights in voting. 

For more information look here.