Answer-first summary for fast verification
Answer: Yes
The solution meets the goal because all listed metrics are appropriate for evaluating a linear regression model. Mean Absolute Error (MAE) measures average prediction error magnitude, Root Mean Squared Error (RMSE) emphasizes larger errors, Relative Absolute Error (RAE) and Relative Squared Error (RSE) compare model performance to a baseline, and Coefficient of Determination (R²) indicates goodness of fit. The community discussion shows strong consensus (100% A votes, multiple upvoted comments) that these metrics collectively provide comprehensive evaluation of regression model performance, covering error magnitude, error distribution, relative performance, and explanatory power. While one comment questioned 'Root Mean Absolute Error' as a valid metric, the actual question uses 'Root Mean Squared Error' (RMSE), which is a standard regression evaluation metric.
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You are creating a model to predict the price of a student's artwork based on the student's length of education, degree type, and art form. You begin by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Mean Absolute Error, Root Mean Square Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Does the solution meet the goal?
A
Yes
B
No
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