The k-Nearest Neighbors (k-NN) algorithm is a straightforward and intuitive classification technique used in machine learning. It operates on the principle that similar data points are likely to have the same class label. When classifying a new data point, the algorithm identifies the ‘k’ nearest data points from the training set based on a distance metric, such as Euclidean distance. The class label of the new data point is then determined by a majority vote among these nearest neighbors.

k-NN is a non-parametric method, meaning it doesn’t assume any underlying data distribution. This characteristic allows it to be versatile and adaptable to various types of datasets. However, its performance can be influenced by the choice of ‘k’ and the distance metric used. Selecting an appropriate ‘k’ is essential, as a very small value might make the model sensitive to noise, while a very large ‘k’ could lead to over-smoothing and less sensitivity to local patterns. Despite its simplicity, k-NN can be highly effective and is widely used for classification tasks where the relationships between data points are relatively clear and consistent.