Understanding K-Nearest Neighbor Algorithm K-Nearest Neighbor (KNN) is a simple machine learning algorithm based on supervised learning, where machines are trained using labeled data containing both input and output. The algorithm assumes similarity between new and existing data to classify the new data into categories with similar features. For example, if an image resembles squares more than circles in its dataset, it will be classified as square by KNN.
How KNN Works: Lazy Learning and Classification Process The lazy learner nature of KNN means it doesn't learn from training datasets but stores them for classification tasks later. During classification, the Euclidean distance formula identifies nearest neighbors among available categories based on proximity to the new data point. Majority voting determines which category this point belongs to; e.g., if two out of three closest points belong to 'square,' then it's categorized as such.
Applying Euclidean Distance Formula in Real Scenarios To predict outcomes like student results using given attributes (e.g., math marks), distances between each student's scores and target values are calculated via Euclidean distance formula √((x2-x1)^2 + (y2-y1)^2). After identifying k-nearest neighbors—those with smallest distances—the majority result among these defines the prediction for unknown cases effectively demonstrating practical use-case scenarios within exams or real-world applications involving categorical predictions.