Which evaluation metric is most appropriate for assessing the performance of a l

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Question: Which evaluation metric is most appropriate for assessing the performance of a linear regression model?

Options:

Correct Answer: Mean Absolute Error (MAE)

Solution:

Mean Absolute Error (MAE) is a suitable metric for evaluating the performance of a linear regression model.

Which evaluation metric is most appropriate for assessing the performance of a linear regression model?

Which evaluation metric is most appropriate for assessing the performance of a linear regression model?

Step 1: Understand that a linear regression model predicts a continuous outcome based on input features.
Step 2: Know that evaluation metrics help us measure how well the model is performing.
Step 3: Learn about different evaluation metrics, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
Step 4: Recognize that Mean Absolute Error (MAE) calculates the average absolute difference between predicted values and actual values.
Step 5: Understand that MAE is easy to interpret because it gives the error in the same units as the predicted values.
Step 6: Conclude that MAE is a suitable metric for evaluating the performance of a linear regression model because it provides a clear measure of prediction accuracy.

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