Designing and Implementing a Data Science Solution on Azure — Question 188

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a model to predict the price of a student's artwork depending on the following variables: 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: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Accuracy, Precision, Recall, F1 score, and AUC.
Does the solution meet the goal?

Answer options

Correct answer: B

Explanation

The proposed solution does not meet the goal because some of the metrics listed, such as Accuracy, Precision, Recall, F1 score, and AUC, are primarily used for classification tasks, not regression. For evaluating a linear regression model, metrics like Mean Absolute Error, Root Mean Squared Error, and R-squared would be more appropriate.