Description: The K-nearest neighbors (K-NN) algorithm is a supervised learning method used for classification or regression on a dataset. Its operation is based on identifying the ‘K’ closest points to a query point within a feature space. This algorithm does not require an explicit model, meaning it makes no assumptions about the data distribution, making it flexible and applicable to a wide variety of problems. The distance between points can be calculated using different metrics, with Euclidean distance being the most common. K-NN is particularly useful in scenarios where the relationship between features is not linear and can be applied in tasks such as image classification, product recommendation, and anomaly detection. However, its performance can be affected by the choice of the value of ‘K’, as a value that is too low can make the model sensitive to noise, while a value that is too high can lead to oversimplification of the classification. Additionally, the algorithm can be computationally expensive on large datasets, as it requires calculating the distance to all points in the training set for each prediction.
