In the realms of regression analysis and machine learning, the Mean Square Error (MSE) serves as a vital metric for gauging the performance of predictive models. It quantifies the average of the squared differences between the predicted and the actual target values in a dataset. The core purpose of the MSE is to evaluate the accuracy of a model's predictions by determining how well they match the true values.
