K-Nearest Neighbors (KNN): A Comprehensive Guide

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The K-nearest neighbors algorithm (KNN) is a very simple yet powerful machine learning model. It assigns a label to a new sample based on the labels of its k closest samples in the training set.

KNN is a lazy learner: it does not build a model from the training data, and all of its computation is deferred until the prediction time.

Despite its simplicity, it provides one of the best performances in many real-world data sets.

KNN is also a very flexible model: it can find decision boundaries of any shape between the classes, and can be used for both classification and regression problems.

K-Nearest Neighbors Classification

The idea behind the KNN classification algorithm is very simple: given a new sample, assign it to the class that is most common among its k nearest neighbors.

The label of a new sample is determined by its k nearest neighbors

The training phase of the algorithm consists of only storing the training samples and their labels, i.e., no model is built from the data.

In the prediction phase, we compute the distances between the test (query) point x and all the stored…