# 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&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

• Thank you very much Feb 26, 2021 at 22:55