DP-100 · Question #41
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…
The correct answer is A. Yes. Yes, the proposed metrics are appropriate for evaluating a linear regression model. Linear regression is a regression task (predicting a continuous value - artwork price), and the correct evaluation metrics for regression are: Mean Absolute Error (MAE - average magnitude of…
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Options
- AYes
- BNo
How the community answered
(46 responses)- A89% (41)
- B11% (5)
Explanation
Yes, the proposed metrics are appropriate for evaluating a linear regression model. Linear regression is a regression task (predicting a continuous value - artwork price), and the correct evaluation metrics for regression are: Mean Absolute Error (MAE - average magnitude of errors), Root Mean Squared Error (RMSE - penalizes large errors more), and Relative Absolute Error (RAE - normalizes MAE against a baseline). These three metrics are the standard regression evaluation metrics available in Azure Machine Learning Studio's Evaluate Model module and are directly meaningful for assessing how accurately the model predicts continuous artwork prices.
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