Which metric would you use to evaluate a regression model's performance that is

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Question: Which metric would you use to evaluate a regression model\'s performance that is sensitive to outliers?

Options:

Correct Answer: Mean Squared Error

Solution:

Mean Squared Error (MSE) is sensitive to outliers because it squares the errors, giving more weight to larger errors.

Which metric would you use to evaluate a regression model's performance that is sensitive to outliers?

Which metric would you use to evaluate a regression model's performance that is sensitive to outliers?

Step 1: Understand what a regression model is. It predicts a continuous outcome based on input features.
Step 2: Learn about performance metrics used to evaluate regression models. Common metrics include Mean Absolute Error (MAE) and Mean Squared Error (MSE).
Step 3: Recognize that MSE calculates the average of the squares of the errors (the differences between predicted and actual values).
Step 4: Understand that squaring the errors means larger errors have a bigger impact on the final score.
Step 5: Conclude that because MSE gives more weight to larger errors, it is sensitive to outliers (extreme values that differ significantly from other observations).

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