Modelo regression is a fundamental statistical method used in various fields, including economics, biology, and inteligencia artificial, to establish the relationship between variables. At its core, análisis de regresión seeks to predict the value of a dependent variable (often referred to as the target) based on the values of one or more independent variables (also known as predictors or features).
Existen varios tipos de modelos de regresión, siendo regresión lineal, where the relationship between the dependent and independent variables is assumed to be linear. In this case, the model is represented by a straight line, described by the equation y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope, and b es la intersección en y.
Formas más complejas de regresión incluyen regresión múltiple, which involves multiple independent variables, and regresión no lineal, which can model relationships that are not linear. Other specialized regression techniques, such as regresión de cresta and regresión LASSO, are used to prevent overfitting by introducing penalties for including too many variables.
El análisis de regresión se usa ampliamente utilizado en aprendizaje automático to build predictive models. The models are trained on historical data, allowing them to learn patterns and make predictions about future or unseen data. Evaluation metrics, such as Mean Squared Error (MSE), are commonly used to assess the performance of regression models, providing insights into their predictive accuracy.