Suppose we have a linear regression model that predicts an item’s price. If the item’s prediction is 8 USD and the actual value is 10 USD, then it is clear that the error is pow(10-8, 2)=4. But how is the error calculated when there are more than two classes?
For example, the model that predicts the numbers trained on the MNIST dataset. In this case we have 10 labels that mark the actual values - range between 0-9. But if we use the sigmoid function for the activation then the range for possible predicted values is between 0-1. Right? How can we compare these values if they are on a different scale? For example, the sigmoid function outputs the values 0.5 and we have to compare it to 3?