mse

Mean Squared Error (MSE), also known as the root mean square error, is a measure of average variance between a predicted value and the actual value. It is a statistical technique used to evaluate the accuracy and fit of a method, such as linear regression.
MSE is calculated by taking the sum of the squares of the differences between the predicted and actual values, then dividing by the number of samples. The result is a relative measure of the variance between the predicted values and the actual values in the dataset.
MSE is typically used as an indicator of the accuracy of a model. Models with lower MSE values have a better fit and are less prone to being over- or underestimated. Generally, a model with a low MSE value is preferable for predicting the values of unknown observations.
MSE can also be used to compare different models. A model with a lower MSE will generally outperform a model with a higher MSE, and it can be used as a guide in selecting the best model for an application.
In summary, MSE is an important tool for evaluating how closely a model's predictions approximate reality and can be used to compare different models to determine which will give the best performance. Understanding MSE and how to use it effectively can help inform decision-making and improve the accuracy of predictive models.