So far, we've seen multiple activation functions, but the one thing that remains constant is the limitation that they can provide only two classes, 0 or 1. Consider the heart disease example:
The neural network predicted 15% not having heart disease and 85% having heart disease, and we set the threshold to 50%. This implies that as soon as one of these percentages exceeds the threshold, we output its index, which would be 0 or 1. In this example, obviously 85% is greater than 50%, so we will output 1, meaning that this person will not have heart disease in the future.
These days, neural networks can actually predict ...
