Mean Square Error (MSE)
Mean Squared Error (MSE) is a regression metric that measures the average squared difference between a model's predicted values and the actual values. It gives greater weight to larger errors, making it useful for evaluating prediction accuracy.
What is Mean Squared Error (MSE)?
MSE is calculated by finding the difference between each predicted and actual value, squaring those differences, and then averaging them. An MSE of 0 indicates perfect predictions. Because errors are squared, larger deviations have a disproportionately greater effect on the final score.
Why is Mean Squared Error (MSE) Important?
MSE is widely used to evaluate and train regression models because it strongly penalizes large prediction errors. This makes it useful when significant errors are particularly undesirable. However, it can be more sensitive to outliers than metrics such as Mean Absolute Error.
Common use cases
MSE is commonly used for regression model evaluation, predictive modeling, time-series forecasting, demand prediction, and other numerical prediction tasks.