KNN Learning Notes

Overview

The k nearest neighbor (k-nearest NEIGHBOR,KNN) classification algorithm can be said to be the simplest machine learning algorithm. It is classified by measuring the distance between different eigenvalue values. Its idea is simple: if a sample is the most similar in the K in the feature space (that is, the nearest neighbor in the feature space), the sample belongs to that category.

Algorithm Summary

The K-Neighbor algorithm is the simplest and most effective algorithm for classifying data. The K-Neighbor algorithm is an instance-based learning, and we must have training sample data close to the actual data when using the algorithm. The K-Neighbor algorithm must hold all data sets, and if the training data set is large, a large amount of storage space must be used. In addition, because distance values must be calculated for each data in the dataset, it can be very time-consuming to actually use it.

KNN Learning Notes