K-Nearest Neighbours

K-Nearest Neighbors (KNN) is a simple and widely used supervised machine learning algorithm. It is used for classification and regression problems in which the goal is to predict a target label or continuous value for a given input data sample. The basic idea behind the KNN algorithm is to determine the K number of nearest data samples in the training set for a given test sample, and then use the majority class or the average value of these K nearest neighbors as the prediction.

In KNN, the value of K (the number of nearest neighbors) is a hyperparameter that needs to be specified beforehand. Larger values of K tend to result in a smoother and more stable prediction, while smaller values of K can lead to overfitting, especially when the training set is small or has a high degree of noise. The choice of K can be optimized through cross-validation or other methods.

KNN is a non-parametric and instance-based algorithm, meaning that it does not make any assumptions about the underlying distribution of the data and does not require any model to be explicitly fit to the data. Instead, the algorithm relies solely on the distances between the data samples to determine the nearest neighbors. This makes KNN a flexible and easy-to-use algorithm for a variety of problems.