K-Nearest Neighbors(KNN) algorithm
Dependencies
Introduction
K-Nearest Neighbors is an algorithm for supervised learning. Where the data is ‘trained’ with data points corresponding to their classification. Once a point is to be predicted, it takes into account the ‘K’ nearest points to it to determine it’s classification.
In this case, we have data points of Class A and B. We want to predict what the star (test data point) is. If we consider a k value of 3 (3 nearest data points) we will obtain a prediction of Class B. Yet if we consider a k value of 6, we will obtain a prediction of Class A.
In this sense, it is important to consider the value of k. But hopefully from this diagram, you should get a sense of what the K-Nearest Neighbors algorithm is. It considers the ‘K’ Nearest Neighbors (points) when it predicts the classification of the test point.
In the Notebook, we learn how to use scikit-learn to implement K-Nearest Neighbors algorithm.