# Understanding logistic regression

I am taking the coursera machine learning course by Andrew Ng and have run into some issues.

I do not understand why the answers are like this?

The equations seem to me the same but the graphs are totally different

• Please provide a logical explanation of why a decision boundary is needed, why a classifier is needed, and why predicted probabilities from logistic regression are deficient. Logistic regression is not a classifier. Jun 1, 2015 at 12:30

• (+1) But the probability that $Y=1$ is not called a "p-value", which has a quite specific meaning in Statistics. Jun 1, 2015 at 11:43