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
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 ?