Description: The K-nearest neighbors regression algorithm (KNN) is a supervised learning method used to predict the value of a data point based on the values of its K nearest neighbors in the feature space. This algorithm is based on the idea that data points that are closer together tend to have similar characteristics. In practice, KNN calculates the distance between the data point to be predicted and all other points in the dataset, selecting the K closest points. The prediction is then made by averaging (in the case of regression) the values of those K neighbors. One of the most notable features of KNN is its simplicity and ease of implementation, making it a popular choice for regression and classification tasks. However, its performance can be affected by the choice of K value and the distance metric used, as well as the dimensionality of the feature space, which can lead to overfitting or underfitting issues. Despite its limitations, KNN remains a valuable tool in the field of machine learning, especially in situations where a small dataset is available and a quick and effective solution is sought.
History: The K-nearest neighbors algorithm was first introduced in 1951 by statistician Evelyn Fix and mathematician Joseph Hodges. However, its popularity grew in the 1970s with the development of machine learning techniques and the availability of more powerful computers. Over the years, KNN has been the subject of numerous studies and improvements, becoming a fundamental method in the field of supervised learning.
Uses: KNN is used in various applications, including price prediction in various markets, classification tasks in different fields, and recommendation systems across diverse platforms. It is also common in data analysis and anomaly detection.
Examples: A practical example of KNN is its use in predicting housing prices, where characteristics such as size, location, and number of rooms of similar properties are analyzed to estimate the value of a new property. Another example is in recommendation systems, where the preferences of similar users are used to suggest products or services.
