The nearest neighbors model refers to a general class of algorithms that aim to make a decision based on the number of nearest neighbors in the training dataset. The nearest neighbors method consists of finding a predefined number of training samples that are close to the distance from the new point and predicting the label. The number of samples can be user defined, consistent, or differ from each other – it depends on the local density of points. The distance can be calculated with any metric measure – the standard Euclidean distance is the most common choice. Neighbor-based methods simply remember all training data.
Finding the nearest neighbors
Getting ready
In this recipe, we will find the nearest neighbors...
