I've started learning about neural networks and I've learned about the perceptron and now the adaline models. One thing I am confused about is the unit step function used to make predictions. With the perceptron, we insert a bias value b into our input function because it allows us to center the step function at 0, correct? That makes sense for the perceptron, however for adaline it appears you must center the step function between the two categories you are trying to classify. In other words, if your model can predict 0 or 1, the step function must be >= 0.5, if it is 1 or -1, it must be >= 0. I haven't been able to find an explanation for why this is, so does anyone have a good answer? I am using Machine Learning with Pytorch and Scikit Learn by Sebastian Raschka as a guide btw.