Example Concept: Common evaluation metrics for regression models include Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. MAE provides a straightforward interpretation of average error in the same units as the target variable, making it useful for understanding typical prediction errors. MSE, which squares the errors, penalizes larger errors more heavily, making it suitable when large errors are particularly undesirable. R-squared offers a measure of how well the model explains the variability of the target data, useful for understanding the model's explanatory power.