29th Sept 2023

Mean Square Error (MSE) is a widely used mathematical metric in statistics and machine learning to measure the average squared difference between the values predicted by a model and the actual observed values in a dataset. To compute MSE, you take the difference between each predicted value and its corresponding actual value, square those differences to eliminate negative values, sum up all the squared differences, and then divide by the number of data points. A smaller MSE indicates that the model’s predictions are closer to the actual values, while a larger MSE suggests greater prediction errors. MSE is particularly useful for assessing the quality of regression models and quantifying their overall accuracy.