Description: Supervised regression is an approach within supervised learning that focuses on predicting continuous values. In this type of model, labeled data is used, where each input is associated with a specific output value. The goal is to learn a function that can map inputs to their corresponding continuous outputs. This process involves identifying patterns and relationships in the data, allowing predictions to be made on new, unseen data. Key features of supervised regression include the use of algorithms such as linear regression, polynomial regression, and support vector regression, among others. The quality of the model is evaluated using metrics such as mean squared error (MSE) or the coefficient of determination (R²), which indicate how well the model fits the data. Supervised regression is fundamental in various fields, as it enables informed decision-making based on quantitative data, facilitating the understanding of complex phenomena and the optimization of processes.
History: Regression has its roots in statistics, with the term ‘regression’ coined by Francis Galton in 1886 while studying the relationship between the heights of parents and their children. Throughout the 20th century, linear regression was formalized as a statistical technique, and with the advancement of computing in the 1960s and 1970s, more complex algorithms for nonlinear and multivariate regression began to be developed. In the era of machine learning, supervised regression has become an essential tool, integrating into various applications and predictive models.
Uses: Supervised regression is used in a wide variety of fields, including economics to predict the prices of goods, in medicine to estimate disease progression, and in engineering to optimize production processes. It is also common in data analysis across industries, where the impact of different variables on quantitative outcomes is sought to be understood.
Examples: An example of supervised regression is predicting the price of a house based on features such as size, location, and number of rooms. Another case is estimating the demand for a product based on historical sales data and economic factors.
