Description: The K-nearest neighbors (K-NN) algorithm is a classification and regression method based on the proximity of data points in a feature space. This algorithm classifies a new data point based on the majority of its nearest neighbors, which are the most similar data points in the training set. The distance between points is commonly measured using metrics such as Euclidean distance, although other metrics can be employed depending on the nature of the data. K-NN is a non-parametric algorithm, meaning it makes no assumptions about the distribution of the data, making it versatile and applicable to a wide variety of problems. Its simplicity and ease of implementation make it a popular choice for classification tasks, especially in scenarios where a small to moderate dataset is available. However, its performance can be affected by the choice of the K value, which determines how many neighbors are considered, as well as by the dimensionality of the data, since a high number of dimensions can complicate the identification of close neighbors. In summary, K-NN is a fundamental algorithm in the fields of machine learning and data analysis, used for data classification and regression in various applications.
History: The K-NN algorithm was first introduced in 1951 by statistician Evelyn Fix and mathematician Joseph Hodges as a method for pattern classification. However, its popularity grew in the 1970s with the development of machine learning techniques and the availability of more powerful computers. Over the years, K-NN has been the subject of numerous research studies and improvements, particularly in terms of optimization and reducing computational complexity. Today, it is widely used in various fields, from computer vision to bioinformatics.
Uses: K-NN is used in a variety of applications, including image classification, pattern recognition, product recommendation, and fraud detection. In the field of machine learning, it is applied for tasks such as classification and regression, as well as in creating recommendation systems that suggest content based on similar preferences from other users.
Examples: A practical example of K-NN is its use in facial recognition systems, where a new face image is classified based on stored face images in a database. Another example is in the classification of plant species, where features such as leaf size and flower shape are used to identify the correct species.
