Answer-first summary for fast verification
Answer: No
The solution does not meet the goal because Accuracy, Precision, Recall, F1 score, and AUC are classification metrics designed for models that predict categorical outcomes. Linear regression is used for predicting continuous numerical values (like artwork prices), so these metrics are inappropriate. The community discussion unanimously supports this with 100% selecting 'No' and providing clear reasoning that these metrics apply to classification tasks, not regression. For evaluating a linear regression model, appropriate metrics would include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Relative Squared Error (RSE), and Coefficient of Determination (R²).
Author: LeetQuiz Editorial Team
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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 start by creating a linear regression model. You need to evaluate the linear regression model.
Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score, and AUC.
Does the solution meet the goal?
A
Yes
B
No
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