4/11/2024 0 Comments Model sets nn![]() Since the value of K is 3, the algorithm will only consider the 3 nearest neighbors to the green point (new entry). We'll then assign a value to K which denotes the number of neighbors to consider before classifying the new data entry. This is represented by the green point in the graph above. The graph above represents a data set consisting of two classes - red and blue.Ī new data entry has been introduced to the data set. With the aid of diagrams, this section will help you understand the steps listed in the previous section. K-Nearest Neighbors Classifiers and Model Example With Diagrams The examples in the sections that follow will help you understand better. Step #4 - Assign the new data entry to the majority class in the nearest neighbors.ĭon't worry if the steps above seem confusing at the moment. Step #3 - Find the K nearest neighbors to the new entry based on the calculated distances. Step #2 - Calculate the distance between the new data entry and all other existing data entries (you'll learn how to do this shortly). The K-NN algorithm compares a new data entry to the values in a given data set (with different classes or categories).īased on its closeness or similarities in a given range ( K) of neighbors, the algorithm assigns the new data to a class or category in the data set (training data). How Does the K-Nearest Neighbors Algorithm Work? We'll also discuss the advantages and disadvantages of using the algorithm. We'll use diagrams, as well sample data to show how you can classify data using the K-NN algorithm. In this article, you'll learn how the K-NN algorithm works with practical examples. The K-Nearest Neighbors (K-NN) algorithm is a popular Machine Learning algorithm used mostly for solving classification problems.
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