Mean Squared Error

Mean Squared Error (MSE) is a widely used metric for evaluating the accuracy of predictive models. It calculates the average of the squares of the differences between the predicted values and the actual values. MSE is particularly useful because it penalizes larger errors more than smaller ones due to the squaring of the differences. Commonly used in regression analysis, MSE helps to assess how well a model fits the data and is often used in conjunction with other metrics for a comprehensive evaluation. A lower MSE indicates a better fit, making it an essential tool in model evaluation.