KNN k~ nearest Neighbor algorithm note

The k~ nearest neighbor algorithm is the simplest machine learning algorithm. It works by comparing each feature of the new data with the characteristics of the data in the sample set, and then extracting the classification label of the data with the most similar characteristics in the sample set. In general, only the first k most similar data in a sample dataset is extracted. Usually k is not greater than 20, and the most recent classification of K most similar data is chosen, the most new one.

However, the k~ neighbor algorithm must hold all the data sets, and if the training data set is large, you must use the measured storage space. In addition, because distance values must be computed for each dataset in the dataset, it can be very time-consuming to actually use them.

KNN k~ nearest Neighbor algorithm note