Hypothesis Representation of Logistic Regression
2 min readJul 20, 2020
Our new form uses the “Sigmoid Function,” also called the “Logistic Function”:
The function g(z), shown here, maps any real number to the (0, 1) interval, making it useful for transforming an arbitrary-valued function into a function better suited for classification.
hθ(x) will give us the probability that our output is 1. For example,hθ(x)=0.7 gives us a probability of 70% that our output is 1. Our probability that our prediction is 0 is just the complement of our probability that it is 1 (e.g. if the probability that it is 1 is 70%, then the probability that it is 0 is 30%).
Above equation simply means that:
