How can I choose the right evaluation metric for my regression model?

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How can I choose the right evaluation metric for my regression model?

Asked on Dec 03, 2025

Answer

Choosing the right evaluation metric for a regression model is crucial for assessing its performance and ensuring it meets the business objectives. The choice depends on the specific goals of the model, the distribution of the target variable, and the importance of different types of errors.

Example Concept: Common evaluation metrics for regression models include Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. MAE provides a straightforward interpretation of average error in the same units as the target variable, making it useful for understanding typical prediction errors. MSE, which squares the errors, penalizes larger errors more heavily, making it suitable when large errors are particularly undesirable. R-squared offers a measure of how well the model explains the variability of the target data, useful for understanding the model's explanatory power.

Additional Comment:

Consider the business context: if large errors are costly, prioritize MSE or RMSE.
Use MAE for a more intuitive understanding of average prediction error.
R-squared is useful for comparing models but doesn't indicate prediction accuracy.
Evaluate models using multiple metrics to get a comprehensive view of performance.

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