👉 Fitted engineering is a statistical method used to estimate the parameters of a model by minimizing the difference between observed data and the predictions made by the model. This approach involves fitting a mathematical function, often a regression model, to a dataset where the goal is to predict outcomes based on input variables. The process typically includes selecting a model form (e.g., linear, polynomial), estimating the parameters that best fit the data using techniques like ordinary least squares (OLS) or maximum likelihood estimation, and then validating the model's performance through metrics such as R-squared or residual analysis. The fitted model provides a simplified representation of the underlying relationships in the data, enabling predictions and insights while accounting for variability and uncertainty.
