k-Nearest Neighbors Algorithm

The k-nearest neighbors algorithm (k-NN) is a supervised Machine Learning algorithm. It's a classification algorithm, determining the class of a sample vector using a sample data.

In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.

The idea is to calculate the similarity between two data points on the basis of a distance metric. Euclidean distance is used mostly for this task.

Image source: Wikipedia

The algorithm is as follows:

Check for errors like invalid data/labels.
Calculate the euclidean distance of all the data points in training data with the classification point
Sort the distances of points along with their classes in ascending order
Take the initial K classes and find the mode to get the most similar class
Report the most similar class

Here is a visualization of k-NN classification for better understanding:

Image source: Wikipedia

The test sample (green dot) should be classified either to blue squares or to red triangles. If k = 3 (solid line circle) it is assigned to the red triangles because there are 2 triangles and only 1 square inside the inner circle. If k = 5 (dashed line circle) it is assigned to the blue squares (3 squares vs. 2 triangles inside the outer circle).

Another k-NN classification example:

Image source: GeeksForGeeks

Here, as we can see, the classification of unknown points will be judged by their proximity to other points.

It is important to note that K is preferred to have odd values in order to break ties. Usually K is taken as 3 or 5.

References

k-nearest neighbors algorithm on Wikipedia