Mean squared Error

The Mean Squared Error (MSE) is a common metric used to evaluate the performance of a model or estimator by measuring the average squared difference between the actual values (observations) and the predicted values (estimates). It quantifies how well the model or estimator fits the data, with lower MSE values indicating a better fit and higher accuracy in predictions.

Interpretation

1. Low MSE: Indicates that the predictions are close to the actual values, meaning the model or estimator is performing well.

2. High MSE: Indicates that there are large differences between the predicted and actual values, meaning the model may not fit the data well.