One machine learning algorithm per day-K-Nearest Neighbor Classification

K-nearest neighbor is simple.

In short, for samples of unknown classes, find the K nearest neighbors in the training set based on a certain computing distance. If most samples of the K Nearest Neighbors belong to which category, it is determined as that category.

The K-voting mechanism can reduce the noise impact.

Since the KNN method mainly relies on a limited number of adjacent samples, rather than the method used to determine the category of the class, for a class set with many overlapping or overlapping classes, KNN is more suitable than other methods.

One disadvantage is that the amount of computing is large, because the distance from each sample to all known samples must be calculated for each sample to be classified to obtain its K Nearest Neighbor points.

One machine learning algorithm per day-K-Nearest Neighbor Classification