Modèle regression is a fundamental statistical method used in various fields, including economics, biology, and intelligence artificielle, to establish the relationship between variables. At its core, analyse de régression 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).
Il existe plusieurs types de modèles de régression, le plus courant étant régression linéaire, 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 est l'ordonnée à l'origine.
Des formes plus complexes de régression incluent régression multiple, which involves multiple independent variables, and régression non linéaire, which can model relationships that are not linear. Other specialized regression techniques, such as régression ridge and régression LASSO, are used to prevent overfitting by introducing penalties for including too many variables.
L'analyse de régression est largement utilisé en apprentissage automatique 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.