There is another kind of regression that we often use in practice—logistic regression. Suppose we want to determine whether or not an email is spam. In this case, our x(s) value could be occurrences of !(s) or the total number of spelling errors in the email. Then, y can take on the value of 1 (for spam) and 0 (for not spam).
In this kind of case, linear regression will simply not work since we are not predicting a real value—we are trying to predict which class the email belongs to.
This will usually end up looking as follows:
As you can see, the data is grouped into two areas—one that represents non-spam and another that represents spam.
We can calculate this as follows:
Here, 
.
However, this only works for binary classification. What if we want to classify multiple classes? Then, we can use softmax regression, which is an extension...
