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Identify two metrics suitable for evaluating a regression model's performance.
A
Coefficient of determination (R2) - Indicates the model's predictive power with a score ranging from -∞ to 1.00.
B
F1 score - A metric for assessing classification models, not applicable to regression.
C
Root mean squared error (RMSE) - Quantifies the average magnitude of the errors between predicted and observed values.
D
Area under curve (AUC) - A classification model evaluation metric, not relevant for regression.
E
Balanced accuracy - NOT_FOUND
