k-NN (k-Nearest Neighbours)

K-NN (K-Nearest Neighbors) is an algorithm used in supervised learning within the field of machine learning. It is a non-parametric form of supervised learning which, instead of making assumptions about data, is based upon the similarity between the classified data points. The output of the algorithm is a class assignment for the data point in question.

K-NN is often used to solve classification and regression problems where the algorithms can learn and identify patterns in datasets. It works by analyzing all of the data points in the dataset and assigning the appropriate label or numerical value based on the data points that are closest to the sample of interest. This method of classification is helpful in cases where the characteristics of data points are not known, as it is then possible to measure the distance between observations and make an educated guess as to the class label.

K-NN is also used in the recommendation systems of many websites. It works by assessing the similarities of a given user’s past selections to other users to make recommendations as to what products the user may be interested in. This allows sites to tailer products and services to individual user interests.

K-NN is a versatile algorithm with widespread applications in areas such as stock markets analysis, facial recognition and crime prevention. As it is very efficient and simple to understand, making it a good starting point for an introduction to machine learning.