Description: KNN (K-Nearest Neighbors) refers to the nearest neighbors algorithm used for classification and regression. This method is based on the idea that data points that are close to each other in feature space tend to have similar labels. In its operation, KNN calculates the distance between a query point and all points in the dataset, selecting the ‘K’ nearest neighbors. Classification is performed through a majority vote among these neighbors, while regression is calculated as the average of the values of the selected neighbors. KNN is a non-parametric algorithm, meaning it makes no assumptions about the data distribution, making it versatile and applicable to a wide variety of problems. However, its performance can be affected by the choice of ‘K’ and the scaling of features, requiring careful selection and preprocessing of data. Additionally, KNN can be computationally expensive, especially in large datasets, as it requires distance calculations for each query point. Despite these limitations, its simplicity and effectiveness in many scenarios have made it a popular tool in the fields of machine learning and data mining.
