As already mention in my one of the previous blog, Classification is one of the two types of Machine learning. It is defined as the graph which has been stated in the form based on discrete values obtained from a observation.
We cannot calculate the classification problem simply by linear regression, we have to use some other efficient method which is know as Logistic Regression.
Our new form uses the “Sigmoid Function,” also called the “Logistic Function”:
| hθ(x)=g(θTx) z=θTx g(z)=1/1+e−z |
The following image shows us what the sigmoid function looks like:
How to plot this and make more efficient will see in the next blog.
The Thesis Whisperer 