A computational tool that quantifies the average squared difference between predicted values and actual values. For example, in regression analysis, it evaluates the performance of a model by calculating the average of the squares of the errorsthe differences between the observed and predicted data points. A result closer to zero indicates a better fit between the model and the data.
This calculation offers a crucial measure of the overall accuracy of predictive models across various fields, including statistics, machine learning, and engineering. Its utility stems from its sensitivity to outliers, penalizing larger errors more heavily than smaller ones. Historically, this approach gained prominence as researchers sought robust methods to minimize deviations and optimize models for greater predictive power and reliability.