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Answer: Coefficient of determination (R2) - Indicates the model's predictive power with a score ranging from -∞ to 1.00., Root mean squared error (RMSE) - Quantifies the average magnitude of the errors between predicted and observed values.
The correct answers are A (Coefficient of determination (R2)) and C (Root mean squared error (RMSE)). These two metrics are commonly used to evaluate regression models. Coefficient of determination (R2) represents the predictive power of the model, with scores ranging from -∞ to 1.00. Root mean squared error (RMSE) measures the average magnitude of the errors between predicted and observed values. The other options, F1 score, Area under curve (AUC), and Balanced accuracy, are metrics used for evaluating classification models, not regression models.
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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
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