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While working with regression fashions, it is very important to pick out an appropriate evaluation metric. It also addresses loss function for regression; few of them are mentioned in Table 2.2. If the distinction between the loss fee and the predicted value is less, then the loss/errors feature could be small and it characterized that the model is most suitable.

Table 2.2 Different evaluation metrics.

Metric	Description	Formula
Mean squared error (MSE)	It is generally used in a regression function, to check how close the regression line to the dataset points is.
Root mean squared error (RMSE)	It is often referred as root mean squared deviation. Its purpose is to find error in the numerical predictive models.
Mean absolute error (MAE)	Similar to MSE, here, also, we take different between actual value and predicted value.
Coefficient of determination (R2)	It is referred to as goodness of fit. The fraction of response/outcome is explained by the model.
Pearson correlation coefficient	It measures the strength of association between two variables.

We can achieve RMSE just by taking square root of MSE. RMSE is very accessible with numerical prediction, to come across if any outliers are messing with the records prediction. Therefore, we select RMSE for version evaluation.