What is a typical evaluation metric used for regression problems?

In regression problems, the primary focus is on predicting continuous outcomes, and traditional classification metrics like accuracy, F1 score, or recall are not applicable. The R2 Score, also known as the coefficient of determination, is a common evaluation metric used in regression tasks to assess the proportion of variance in the dependent variable that can be explained by the independent variables in the model.

A high R2 Score indicates that a significant portion of the variability in the output can be captured by the model, while an R2 Score of zero suggests that the model does not explain any variability, and a negative score indicates that the model is performing worse than a horizontal line representing the mean of the target variable.

The other metrics mentioned, such as accuracy, F1 Score, and recall, are designed for classification tasks where the output is categorical rather than continuous. Hence, they would not provide meaningful insights into the performance of a regression model. This makes the R2 Score the most appropriate evaluation metric for assessing regression models.