What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Coefficient of determination (R2) and Root Mean Squared Error (RMSE) are two widely used metrics for evaluating regression models.

Submitted by khalil_dz· Mar 30, 2026

Question

What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options

Acoefficient of determination (R2)
BF1 score
Croot mean squared error (RMSE)
Darea under curve (AUC)
Ebalanced accuracy

Why each option

Coefficient of determination (R2) and Root Mean Squared Error (RMSE) are two widely used metrics for evaluating regression models.

Acoefficient of determination (R2)

BF1 score

F1 score is a metric used to evaluate classification models, combining precision and recall, and is not applicable to regression tasks.

Croot mean squared error (RMSE)

Darea under curve (AUC)

Area Under Curve (AUC) is a metric primarily used for evaluating the performance of binary classification models, not regression models.

Ebalanced accuracy

Balanced accuracy is a classification metric, especially useful for imbalanced datasets, and is not relevant for evaluating regression model performance.

Concept tested: Regression model evaluation metrics

Source: https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/evaluate-model?view=azureml-inference-oss-sdk#metrics-for-regression-models

Topics

#Regression model evaluation#Model evaluation metrics

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