nerdexam
Microsoft

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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Question

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, Relative Squared Error, and the Coefficient of Determination. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(46 responses)
  • A
    89% (41)
  • B
    11% (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.

Topics

#Linear Regression#Model Evaluation#Regression Metrics#Performance Metrics

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