Decision boundary- Logistic Regression

In order to get our discrete 0 or 1 classification, we can translate the output of the hypothesis function as follows

The way our logistic function g behaves is that when its input is greater than or equal to zero, its output is greater than or equal to 0.5:

To understand this in a better way, see the pictorial representation below:

The decision boundary is the line that separates the area where y = 0 and where y = 1. It is created by our hypothesis function.

Example1:

Example 2:

Example 3:

In this case, our decision boundary is a straight vertical line placed on the graph where x1​=5 and everything to the left of that denotes y = 1, while everything to the right denotes y = 0.