How are the parameters of the model, the weights w and bias b, learned? Logistic regression is an instance of supervised classification in which we know the correct label y (either 0 or 1) for each observation x. What the system produces via Eq. 5.5 isŷ, the system's estimate of the true y. We want to learn parameters (meaning w and b) that makeŷ for each training observation as close as possible to the true y.