Identify two metrics suitable for evaluating a regression model's performance.

Exam-Like

Coefficient of determination (R2) - Indicates the model's predictive power with a score ranging from -∞ to 1.00.

44.9%

F1 score - A metric for assessing classification models, not applicable to regression.

3.1%

Root mean squared error (RMSE) - Quantifies the average magnitude of the errors between predicted and observed values.

48.4%

Area under curve (AUC) - A classification model evaluation metric, not relevant for regression.

2.7%

Balanced accuracy - NOT_FOUND_

0.9%

Microsoft Azure AI Fundamentals - AI-900