Gradient Descent for multiple feature linear regression

[Christian_Simonis]

This thread addresses exactly this question:

Decision boundary vs prediction f_wb in logistic regression

Hi, I struggle to understand how decision boundary is different from sigmoid function. In the example f_{\vec{w},b}(\vec{x})=g(w_1x_1 + w_2x_2 + b) where w1 = 1, w2 = 1, b = -3, x1 + x2 = 3 so the decision boundary is a straight line on a graph of x2 plotted against x1, f(x1) = -x1 + 3. But g(z) is a sigmoid function. So how the heck are we talking about a linear equation? I am utterly missing the abstraction transition here. If I am to calculate what is the probability that a point is with…

Summary: after training your model, you can dimension your threshold based in relevant metrics, e.g. false positive rate and false negative rate.

Hope that helps!

Best
Christian