Final answer:
The F1 score is not used for evaluating the performance of a regression model; it is used for classification models. Metrics like AIC, R-squared, and Mean Squared Error are used for regression models.
Step-by-step explanation:
The metric not used for evaluating the performance of a regression model among the options provided is the F1 score. The F1 score is typically used for evaluating classification models, not regression models. Regression models are typically evaluated using metrics such as the Akaike Information Criterion (AIC), which deals with the trade-off between the goodness of fit of the model and the complexity of the model; the coefficient of determination (R-squared), which indicates the proportion of the variance in the dependent variable that is predictable from the independent variable(s); and the Mean Squared Error (MSE), which measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value.
For example, in a regression situation where we predict final exam grades based on grades of a third exam with the line of best fit ŷ = -173.51 + 4.83x, and a correlation coefficient of r = .6631, we find r² to be approximately .4397 or 44%. This indicates that approximately 44 percent of the variation in final exam grades can be explained by the variation in the grades on the third exam, using the regression line.