Data Science, Machine Learning, Regression, Evaluation Metric, Linear Regression.
and these squared differences are summed up. We then divide it by the total number n of data samples. By squaring the errors, we ensure that the MSE score is always greater than zero. It also gives more weight to outliers to emphasize their influence on the final score. This is particularly useful