Machine learning with the "diabetes" data set in R – Towards Data Science

We'll begin by applying the k-nearest neighbors method of classifying patients by their similarity to other patients. For this method (and all subsequent methods), we'll start by separating the data set into "training" and "test" sets. We'll build our model based on the relationship between the predictors and the outcome on the training set, then use the model's specifications to predict the outcome on the test set. We can then compare our predicted outcomes to the test set's actual diabetes status to give us a measure of model accuracy. For k-nearest neighbors, we compute the outcome for each test case by comparing that case to the "nearest neighbors" in the training set.