How to Use Python for Learning Vector Quantization From Scratch

Learning vector quantization is a supervised learning algorithm that is mainly used for classification problems.

Learning vector quantization is a supervised learning algorithm for classifying input data using simple vector and distance calculations. Vector quantization is an unsupervised destiny estimator.

LVQ is related to the KNN algorithm. One of the disadvantages of KNN is that the entire training dataset needs to be analyzed. On the other hand, LVQ allows you to select the number of training samples of your choice. It reduces memory utilization.

One of the important advantages of learning vector quantization is the predefined model complexity that is determined by the prototypes. It uses limited memory and offers faster calculations.