Typically, the $R^2$ score is sufficient for evaluating a regression model. Alternative evaluation metrics include:

• Mean Absolute Error (MAE): The absolute difference between target and predicted values, representing the expected value of $L_1$ norm loss.
• Mean Squared Error (MSE): The squared difference between target and predicted values, representing the expected value of $L_2$ norm loss.
• Median Absolute Error: Uses the median of the error distribution rather than the mean, making it more robust to outliers.