Logistic Regression: Decision Boundary
In order to get our discrete 0 or 1 classification, we can translate the output of the hypothesis function as follows:
Recall that for the logistic function , we have when .
The decision boundary is the line that separates the area where y = 0 and where y = 1. It is created by our hypothesis function using the equation:
The input to the sigmoid function doesn't need to be linear, and could be a function that describes a circle (e.g. ) or any other shape to fit our data.