Mean Squared Error

Mean Squared Error (MSE) is a measure used to assess how well a model predicts outcomes. It calculates the average of the squares of the differences between the predicted values and the actual values. By squaring these differences, MSE gives greater weight to larger errors, highlighting significant mistakes. A lower MSE indicates that the model's predictions are closer to the actual results, suggesting better performance. Essentially, MSE helps quantify how accurate a model is, making it easier to compare different models or approaches in various fields like forecasting, statistics, and machine learning.