KNN Algorithm
Introduction to K-Nearest Neighbors (KNN) K-Nearest Neighbors (KNN) is a simple, non-parametric, and lazy learning algorithm used for both classification and regression tasks. It operates on the principle that similar instances exist in close proximity to one another in the feature space. How KNN Works Distance Calculation : For a given data point (query), calculate the distance between this point and all other points in the training set. Common distance metrics include: Euclidean Distance : ∑ i = 1 n ( x i − y i ) 2 \sqrt{\sum_{i=1}^{n} (x_i - y_i)^2} Manhattan Distance : ∑ i = 1 n ∣ x i − y i ∣ \sum_{i=1}^{n} |x_i - y_i| Minkowski Distance : Generalized distance metric. Selecting Neighbors : Choose the k k closest points (neighbors) to the query point based on the calculated distances. Voting for Classification : For classification tasks, use the majority class among the k k neighbors to determine the class of the query point. Tie-breaking : If there is a tie, use additional ...