Iterative Method of Adjusting Parameters in kNN via Minkowski Metric

In today’s world, where solutions from the last century are no longer enforced, there is a constant demand for newer, more efficient ways to analyze data. An example of such an application is the k-nearest neighbors (k-nn) mechanism. In this article, this mechanism will be proposed, improved by the possibility of finding the optimal number of neighbors and the coefficient m for the Minkowski function used in it to calculate the distance between points. This mechanism is automated, which allows you to use different parameters for the Minkowski function and determine the accuracy for a different number of neighbors in an automatic way. From these accuracies, the ranking system selects the best values for the parameter m, which defines the dimension of the space in the Minkowski function, and the best number of nearest neighbors. The number of nearest neighbors checked and the value of the m parameter can be set independently, which allows you to check various combinations of the m parameter and the number of nearest neighbors.