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in the last video we manually split our data into a single test set and evaluated out-of-sample error once however this process is a little fragile the presence or absence of a single outlier can vastly change our out-of-sample our MSE a better approach then a simple train test split is using multiple test sets and averaging out-of-sample error which gives us a more precise estimate of the true out-of-sample error one of the most common approaches for multiple test sets is known as cross-validation in which we split our data into ten folds or train test splits we create these folds in such a way that each point in our data set occurs in exactly one test set this gives us ten test sets and better yet means that every single point in our data set occurs exactly once in other words we get a test set that is the same size as our training set but is composed of out-of-sample predictions we assign each row to its test set randomly to avoid any kinds of systematic biases in our data this is o
