MSE, abbreviated for Mean Squared Error, is a statistical term used in various fields, such as mathematics, statistics, and computer science. It is a measure that quantifies the average difference between observed values and predicted values, commonly used for assessing the accuracy of a regression or forecasting model.
The MSE is calculated by taking the average of the squared differences between the actual and predicted values. First, for each data point, the difference between the observed value and the predicted value is calculated. These differences are then squared to avoid negative values and to emphasize larger differences. The squared differences are finally averaged across all data points.
The MSE provides a numerical measure of how well a model predicts the outcome variable. A lower MSE indicates a better fit between the model and the observed data, implying higher accuracy in predicting future values. Conversely, a higher MSE implies a larger discrepancy between the model's predictions and the actual observations.
The MSE is particularly useful in comparing different models or methods when determining the most effective one. It serves as an objective criterion for evaluating and selecting the best model by minimizing the difference between predicted and observed values.
Overall, the MSE plays a crucial role in assessing the performance and reliability of predictive models, making it an essential tool for researchers, statisticians, and data analysts.
