it turns out that when we use polynomial fitwe end up with an overall score that' even lower than our original run sothis tells us that the polynomial kernel is probably overfitting when we use -fold cross-validation it reveals an actual lower score than with our linear kernel the important point here is that if we had just used single train/test splitwe wouldn' have realized that we were overfitting we would have actually gotten the same result if we just did single train/test split here as we did on the linear kernel sowe might inadvertently be overfitting our data thereand not have even known it had we not use kfold cross-validation sothis is good example of where -fold comes to the rescueand warns you of overfittingwhere single train/test split might not have caught that sokeep that in your tool chest if you want to play around with this some morego ahead and try different degrees soyou can actually specify different number of degrees the default is degrees for the polynomial kernelbut you can try different oneyou can try two does that do betterif you go down to onethat degrades basically to linear kernelrightsomaybe there is still polynomial relationship and maybe it' only second degree polynomial try it out and see what you get back that' -fold cross-validation as you can seeit' very easy to use thanks to scikit-learn it' an important way to measure how good your model is in very robust manner data cleaning and normalisation nowthis is one of the simplestbut yet it might be the most important section in this whole book we're going to talk about cleaning your input datawhich you're going to spend lot of your time doing how well you clean your input data and understand your raw input data is going to have huge impact on the quality of your results maybe even more so than what model you choose or how well you tune your models sopay attentionthis is important stuffcleaning your raw input data is often the most importantand timeconsumingpart of your job as data scientist