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K Nearest Neighbors is an algorithm with secrets. On one hand, the algorithm itself is as straightforward as possible: find the labeled points nearest the point that you need to predict, and make a prediction that\u2019s the average of their answers. On the other hand, what does \u201cnearest\u201d mean when you\u2019re dealing with complex data? How do you decide whether a man and a woman of the same age are \u201cnearer\u201d to each other than two women several years apart? What if you convert all your monetary columns from dollars to cents, your distances from miles to nanometers, your weights from pounds to kilograms? Can your definition of \u201cnearest\u201d hold up under these types of transformations? We\u2019re discussing all this, and more, in this week\u2019s episode.