When predicting continuous variables, such as apartment prices, it's essential to evaluate the model's performance using appropriate regression metrics. The Mean Absolute Error (MAE) is a widely used metric for this purpose.

Understanding Mean Absolute Error (MAE):

MAE measures the average magnitude of errors in a set of predictions, without considering their direction. It calculates the average absolute difference between predicted values and actual values, providing a straightforward interpretation of prediction accuracy.

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Advantages of MAE:

Interpretability:MAE is expressed in the same units as the target variable, making it easy to understand.

Robustness to Outliers:Unlike metrics that square the errors (e.g., Mean Squared Error), MAE does not disproportionately penalize larger errors, making it more robust to outliers.

Comparison with Other Metrics:

Accuracy, AUC, F1 Score:These metrics are designed for classification tasks, where the goal is to predict discrete labels. They are not suitable for regression problems involving continuous target variables.

Mean Squared Error (MSE):While MSE also measures prediction errors, it squares the differences, giving more weight to larger errors. This can be useful in certain contexts but may be sensitive to outliers.

Conclusion:

For evaluating the performance of a model predicting apartment prices—a continuous variable—MAE is an appropriate and effective metric. It provides a clear indication of the average prediction error in the same units as the target variable, facilitating straightforward interpretation and comparison.

References:

Regression Metrics – GeeksforGeeks

Evaluation Metrics for Your Regression Model – Analytics Vidhya

Regression Metrics for Machine Learning – Machine Learning Mastery