Description: K nearest neighbors (KNN) is a non-parametric method used for classification and regression, predicting the value of a point based on the values of its k nearest neighbors. This algorithm is based on the idea that data points that are close to each other tend to have similar characteristics. KNN is easy to understand and implement, making it a popular choice in the field of machine learning. Unlike other methods that require assumptions about the distribution of data, KNN does not make such assumptions, allowing it to adapt to a variety of problems. However, its performance can be affected by the choice of the value of k, as well as by the scale of the features, necessitating proper normalization of the data. Additionally, KNN can be computationally expensive, especially with large datasets, as it requires calculating the distance between the point to be classified and all other points in the dataset. Despite these limitations, KNN remains a valuable tool in a data scientist’s toolbox, used in various applications ranging from product recommendation to fraud detection.
History: The K nearest neighbors 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 more powerful computers that allowed its implementation on larger datasets. Over the years, KNN has been the subject of numerous research studies and improvements, especially in the field of machine learning and artificial intelligence, where it has been used in a variety of practical applications.
Uses: KNN is used in various applications, including image classification, fraud detection, product recommendation, and general data analysis. In the healthcare field, it is applied to classify diseases based on symptoms and patient data. It is also used in voice recognition systems and market segmentation, where it helps identify groups of consumers with similar characteristics.
Examples: A practical example of KNN is its use in recommendation systems, such as those used by streaming platforms to suggest movies or music to users based on their preferences and the choices of similar users. Another example is in medical diagnosis, where KNN can help classify patients into risk groups based on their symptoms and medical history.
