Which evaluation metric is most appropriate for regression models during deploym

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Question: Which evaluation metric is most appropriate for regression models during deployment?

Options:

Correct Answer: Mean Absolute Error (MAE)

Solution:

Mean Absolute Error (MAE) is a suitable evaluation metric for regression models, as it measures the average magnitude of errors in predictions.

Which evaluation metric is most appropriate for regression models during deployment?

Which evaluation metric is most appropriate for regression models during deployment?

Step 1: Understand that regression models predict continuous values, like prices or temperatures.
Step 2: Learn about evaluation metrics, which help us measure how well our model is performing.
Step 3: Identify that Mean Absolute Error (MAE) is one of the metrics used for regression models.
Step 4: Know that MAE calculates the average of the absolute differences between predicted values and actual values.
Step 5: Realize that MAE is easy to understand because it gives a straightforward measure of prediction error in the same units as the target variable.
Step 6: Conclude that using MAE during deployment helps in assessing the model's accuracy effectively.

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