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Which metric would be most appropriate for evaluating a regression model?

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Question: Which metric would be most appropriate for evaluating a regression model?

Options:

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

Correct Answer: Mean Absolute Error

Solution: 

Mean Absolute Error (MAE) is a common metric for evaluating regression models, measuring the average magnitude of errors in a set of predictions without considering their direction.

Which metric would be most appropriate for evaluating a regression model?

Practice Questions

Q1
Which metric would be most appropriate for evaluating a regression model?
  1. Accuracy
  2. F1 Score
  3. Mean Absolute Error
  4. Confusion Matrix

Questions & Step-by-Step Solutions

Which metric would be most appropriate for evaluating a regression model?
  • Step 1: Understand what a regression model does. It predicts a continuous value based on input data.
  • Step 2: Learn about evaluation metrics. These are ways to measure how well the model is performing.
  • Step 3: Know that Mean Absolute Error (MAE) is one of these metrics. It tells us how far off our predictions are from the actual values.
  • Step 4: Realize that MAE calculates the average of the absolute differences between predicted values and actual values.
  • Step 5: Understand that MAE does not consider whether the predictions are above or below the actual values, only the size of the errors.
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