mean_squared_error, r2_score

Both mean_squared_error and r2_score are metrics used to evaluate the performance of regression models.

- mean_squared_error (MSE) is a measure of the average squared difference between the predicted and actual values. It is calculated as the average of the squared differences between the predicted and actual values of the target variable. A lower value of MSE indicates a better fit of the model to the data.

- r2_score (R-squared) is a measure of the goodness of fit of the model. It represents the proportion of variation in the target variable that is explained by the model. The value of R-squared ranges from 0 to 1, with a higher value indicating a better fit of the model to the data.

In general, MSE is used to evaluate the accuracy of the model, while R-squared is used to evaluate the overall performance of the model in terms of how well it explains the variation in the target variable.