[D] How important is it to adjust the bias in a perceptron learning algorithm? • r/MachineLearning

The thing to realize is that for a two-dimensional problem, the decision boundary is a line, and a line can be uniquely identified by two parameters (e.g. its slope and its y-intercept, or equivalently x-intercept). If you set up the perceptron as having two weights and also a tunable bias, that's three parameters, more than you actually need. However, if you fixed the bias to zero, you'd lose the ability to represent lines that don't pass through the origin. But as long as bias is fixed to a non-zero constant (1 is as good as any other, modulo taking a different number of steps to converge depending on your initialization) your representational power is maintained.