in this caseit contains all the species for each flower uftu@tj[ says what percentage do we want to train versus test so means we're going to extract of that data randomly for testing purposesand use for training purposes what this gives us back is datasetsbasicallya training dataset and test dataset for both the feature data and the target data so @usbjo ends up containing of our iris measurementsand @uftu contains of the measurements used for testing the results of our model @usbjo and @uftu contain the actual species for each one of those segments then after that we go ahead and build an svc model for predicting iris species given their measurementsand we build that only using the training data we fit this svc modelusing linear kernelusing only the training feature dataand the training species datathat istarget data we call that model dmg thenwe call the tdpsf function on dmg to just measure its performance against our test dataset sowe score this model against the test data we reserved for the iris measurementsand the test iris speciesand see how well it doesit turns out it does really wellover of the timeour model is able to correctly predict the species of an iris that it had never seen beforejust based on the measurements of that iris so that' pretty coolbutthis is fairly small datasetabout flowers if remember right sowe're only using of flowers for training and only of flowers for testing these are still fairly small numbersso we could still be overfitting to our specific train/test split that we made solet' use -fold cross-validation to protect against that it turns out that using -fold cross-validationeven though it' more robust techniqueis actually even easier to use than train/test sothat' pretty coolsolet' see how that works fhjwfdsptt@wbm@tdpsfbnpefmuiffoujsfebubtfuboejutsfbmwbmvftboeuifovncfspggpmet tdpsft dsptt@wbmjebujpodsptt@wbm@tdpsf dmgjsjtebubjsjtubshfudw  sjouuifbddvsbdzgpsfbdigpme qsjoutdpsft "oeuifnfbobddvsbdzpgbmmgpmet qsjoutdpsftnfbo