Which model should be considered better based on Mean Squared Error (MSE) between Model A (MSE 1200) and Model B (MSE 950)?

Mean Squared Error (MSE) is a metric used to evaluate the quality of a predictive model. It measures the average of the squares of the errors—that is, the difference between the predicted values and the actual values. A lower MSE indicates that the model's predictions are closer to the actual values, which reflects better performance.

In this case, Model B has an MSE of 950, while Model A has an MSE of 1200. The fact that Model B has a lower MSE signifies that it makes predictions that are, on average, more accurate than those made by Model A. Therefore, based on the MSE metric alone, Model B should indeed be considered the better option because it indicates a higher quality of predictions.

This understanding of MSE plays a crucial role in model evaluation, especially in contexts where prediction accuracy is paramount. Thus, when comparing multiple models, opting for the one with the lower MSE is a best practice in model selection, illustrating why Model B is the preferable choice in this scenario.