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Which evaluation metric is commonly used to assess the performance of a linear r

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Question: Which evaluation metric is commonly used to assess the performance of a linear regression model?

Options:

  1. Accuracy
  2. F1 Score
  3. Mean Absolute Error (MAE)
  4. Confusion Matrix

Correct Answer: Mean Absolute Error (MAE)

Solution: 

Mean Absolute Error (MAE) is a common metric for evaluating the performance of regression models, measuring the average magnitude of errors in predictions.

Which evaluation metric is commonly used to assess the performance of a linear r

Practice Questions

Q1
Which evaluation metric is commonly used to assess the performance of a linear regression model?
  1. Accuracy
  2. F1 Score
  3. Mean Absolute Error (MAE)
  4. Confusion Matrix

Questions & Step-by-Step Solutions

Which evaluation metric is commonly used to assess the performance of a linear regression model?
  • Step 1: Understand that a linear regression model predicts a continuous value.
  • Step 2: Know that we need a way to measure how well the model's predictions match the actual values.
  • Step 3: Learn about evaluation metrics, which are tools to assess model performance.
  • Step 4: Identify that Mean Absolute Error (MAE) is one of the common metrics used for regression models.
  • Step 5: Understand that MAE calculates the average of the absolute differences between predicted values and actual values.
  • Step 6: Realize that a lower MAE indicates better model performance.
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