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Microsoft

DP-100 · Question #43

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 B. No. The proposed solution includes 'Accuracy' as a metric, which is a classification metric-not a regression metric. For a linear regression model, valid evaluation metrics include Relative Squared Error, Relative Absolute Error, Coefficient of Determination (R²), Mean Absolute…

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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: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(31 responses)
  • A
    19% (6)
  • B
    81% (25)

Explanation

The proposed solution includes 'Accuracy' as a metric, which is a classification metric-not a regression metric. For a linear regression model, valid evaluation metrics include Relative Squared Error, Relative Absolute Error, Coefficient of Determination (R²), Mean Absolute Error, and Root Mean Squared Error. Since 'Accuracy' is inappropriate for regression tasks, the metric set is invalid and the solution does not meet the goal.

Topics

#Model Evaluation#Regression Metrics#Classification Metrics#Linear Regression

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