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Microsoft

DP-100 · Question #42

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. No, the proposed metrics are wrong for this task. Accuracy, Precision, Recall, F1 score, and AUC are classification metrics - they measure how well a model assigns data points to discrete categories. Linear regression is a regression model that predicts a continuous numerical…

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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: Accuracy, Precision, Recall, F1 score and AUC. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(20 responses)
  • A
    20% (4)
  • B
    80% (16)

Explanation

No, the proposed metrics are wrong for this task. Accuracy, Precision, Recall, F1 score, and AUC are classification metrics - they measure how well a model assigns data points to discrete categories. Linear regression is a regression model that predicts a continuous numerical output (artwork price). Applying classification metrics to a regression model is conceptually incorrect and technically not possible without first thresholding the output into classes. The correct metrics for evaluating a linear regression model are regression metrics such as Mean Absolute Error, Root Mean Squared Error, R-squared (coefficient of determination), and Relative Absolute Error.

Topics

#Regression#Classification#Evaluation Metrics#Model Evaluation

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