KNN, or K Nearest Neighbors, is an acronym. This method, as its name implies, attempts to categorize a new situation using K neighboring points.
For K, the practical range is 2 to 10. If K=3, KNN will attempt to find the three closest points.
For every new point, it consists of 3 straightforward steps.
The most K comparable (closest) spots should be located.
In those K points, determine the number of each class.
Assign the new point to the class that appears the most frequently among these K points.
Take the mean of the closest “K” points for calculating regression.
Distance between two points can be calculated using any one of the below methods.
- Euclidean Distance: Take the difference between the coordinates of points and add it after squaring.
- Manhattan Distance: The sum of absolute differences between the coordinates of points.