In multiple regression, we predict y based on the different features present in ______

In multiple regression, we predict $y$ based on the different features present in the independent variables (or predictor variables). These independent variables can take various forms, including continuous and categorical data, and they serve as the inputs that influence the outcome variable $y$, which is the dependent variable. The objective of multiple regression is to model the relationship between $y$ and these independent variables by finding the best-fit line (or hyperplane in higher dimensions) that minimizes the difference between the observed values and the values predicted by the model, resulting in an accurate estimate of $y$ for given predictors. This method is widely applied in many fields, including economics, medicine, and social sciences, to predict real-world outcomes based on multiple influencing factors.