now we can use linear svc (svc is form of svm)to actually partition that into clusters we're going to use svm with linear kerneland with value of  is just an error penalty term that you can adjustit' by default normallyyou won' want to mess with thatbut if you're doing some sort of convergence on the right model using ensemble learning or train/testthat' one of the things you can play with thenwe will fit that model to our feature dataand the actual classifications that we have for our training dataset gspntlmfbsojnqpsutwnebubtfut twd twn lfsofm mjofbs gju  solet' go ahead and run that don' want to get too much into how we're actually going to visualize the results herejust take it on faith that qmpu sfejdujpot is function that can plot the classification ranges and svc