Description: A fitted model is a statistical model that has been trained on a specific dataset to make predictions or inferences about new data. This fitting process involves calibrating the model’s parameters to best match the characteristics and patterns present in the training data. Fitted models are fundamental in predictive analytics, as they allow analysts and data scientists to estimate future events based on historical information. The quality of a fitted model is evaluated through metrics such as mean squared error or accuracy, which indicate how well the model performs compared to actual data. Additionally, a fitted model can be simple, like linear regression, or complex, like a machine learning model, depending on the nature of the problem and the amount of available data. The ability of a fitted model to generalize to new data is crucial, as a model that fits too closely to the training data may result in overfitting, limiting its usefulness in real-world situations. Therefore, the fitting and validation process is essential to ensure that the model is robust and reliable in its predictions.
