Modelo regression is a fundamental statistical method used in various fields, including economics, biology, and inteligência artificial, to establish the relationship between variables. At its core, análise de regressão 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).
Existem vários tipos de modelos de regressão, sendo o mais comum regressão linear, 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 é o intercepto y.
Formas mais complexas de regressão incluem regressão múltipla, which involves multiple independent variables, and regressão não linear, which can model relationships that are not linear. Other specialized regression techniques, such as regressão de crista and regressão LASSO, are used to prevent overfitting by introducing penalties for including too many variables.
A análise de regressão é amplamente usada em aprendizado de máquina 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.