Multiple Regression, Classification and Boundary Poins I have two gangs which are doing crimes. And i want to classify them.
Lets say I'm looking for a regression function:
 M(x1, x2) = w1x1 + w2x2 + w3 

Now I have found all three parameters w1, w2, w3.
Now I want to do classification. I get some boundary points which look like a line and they separate two classes from each other. Should I do another regression over that boundary points so that i have a exact line for my separation?
Because lets say i want Point(5,3). I want to know if its more likely that the crime is done from Gang A or B. But I have just some boundary points to separate. Should I use them for a regression?
I think you want a clustering algorithm rather than regression. You will have a decision boundary between clusters of data points which will determine whether that particular point e.g. 5,3 belongs to group (cluster) A or group (cluster) B
Fit your clustering model in x1 vs x2 feature space. Take the image below, we have x1 and x2 as the x&amp;y axis. And then the black lines are the decision boundaries, essentially the model.

You can of course just cluster between 2 groups also, as in your case.
You can checkout different clustering algorithms here