we have model alreadythe svc model that we defined for this predictionand all you need to do is call dsptt@wbm@tdpsf on the dsptt@wbmjebujpo package soyou pass in this function model of given type (dmg)the entire dataset that you have of all of the measurementsthat isall of my feature data (jsjtebuband all of my target data (all of the species)jsjtubshfu want dw which means it' actually going to use different training datasets while reserving for testing basicallyit' going to run it timesand that' all we need to do that will automatically evaluate our model against the entire datasetsplit up five different waysand give us back the individual results if we print back the output of thatit gives us back list of the actual error metric from each one of those iterationsthat iseach one of those folds we can average those together to get an overall error metric based on -fold cross-validationwhen we do this over foldswe can see that our results are even better than we thought accuracy so that' pretty coolin factin couple of the runs we had perfect accuracy so that' pretty amazing stuff now let' see if we can do even better we used linear kernel beforewhat if we used polynomial kernel and got even fancierwill that be overfitting or will it actually better fit the data that we havethat kind of depends on whether there' actually linear relationship or polynomial relationship between these petal measurements and the actual species or not solet' try that outdmg twn lfsofm qpmz gju @usbjo @usbjo tdpsft dsptt@wbmjebujpodsptt@wbm@tdpsf dmgjsjtebubjsjtubshfudw qsjoutdpsft qsjoutdpsftnfbo we'll just run this all againusing the same technique but this timewe're using polynomial kernel we'll fit that to our training datasetand it doesn' really matter where you fit to in this casebecause dsptt@wbm@tdpsf will just keep re-running it for you