There are other variations of K-nearest neighbors that I'd like to quickly go through before moving on to the next section. These algorithms are less commonly used, although they have their merits as well as certain disadvantages.
Radius neighbors
Contrary to the K-nearest neighbors algorithm, where a certain number of neighbors are allowed to vote, in radius neighbors, all the neighbors within a certain radius participate in the voting process. By setting a predefined radius, the decisions in sparser neighborhoods are based on fewer neighbors than the ones made in denser neighborhoods. This can be useful when dealing with imbalanced classes. Furthermore, by using the haversine formula as our metric, we can use this algorithm to recommend nearby venues or gas stations on a map to the users. Both radius neighbors and K-nearest neighbors can give closer data points more voting power than distant ones by specifying the algorithm...
