Description: K-Nearest Neighbors (KNN) regression is a supervised learning technique used to predict continuous values based on the proximity of data points in a multidimensional space. This method is based on the idea that similar data points tend to be close to each other. In KNN regression, a number ‘k’ of nearest neighbors to an unknown data point is selected, and the mean (or a weighted combination) of their output values is calculated to make a prediction. This technique is particularly useful in situations where the relationship between variables is not linear and can be applied to a variety of problems across different domains. The simplicity of the algorithm, along with its ability to adapt to different types of data, makes it a popular choice in the field of machine learning. However, its performance can be affected by the choice of ‘k’, the scaling of the data, and the presence of noise in the dataset. Despite its limitations, KNN regression remains a valuable tool in data analysis and predictive modeling, especially in contexts where an intuitive interpretation of results is required.
History: The K-Nearest Neighbors technique was introduced in 1951 by statistician Evelyn Fix and mathematician Joseph Hodges, who used it for classifying data in a pattern recognition context. Over the years, the method has evolved and adapted to various applications in the field of machine learning and artificial intelligence. In the 1970s, the algorithm began to gain popularity in the research community, and its implementation was facilitated by the development of more powerful computers and access to large datasets. Today, KNN is widely used in data mining applications, image recognition, and recommendation systems, among others.
Uses: K-Nearest Neighbors regression is used in a variety of applications, including price prediction in diverse markets, estimation of demand for products, and data analysis in various fields such as biomedicine. It is also applied in recommendation systems, where user preferences are predicted based on similar behaviors of other users. Additionally, it is used in domains such as meteorology to predict weather conditions based on historical data.
Examples: A practical example of KNN regression is predicting housing prices, where characteristics such as size, location, and number of rooms of similar properties are analyzed to estimate the value of a particular house. Another example is the use of KNN in recommendation systems, where titles are suggested to users based on ratings from other users with similar tastes.
