When comparing two regression models for predicting housing prices, which model should be chosen based on R-squared and Mean Absolute Error metrics?

Choosing the model based on R-squared and Mean Absolute Error (MAE) involves understanding the significance of these metrics in the context of regression analysis. The correct choice emphasizes the importance of the R-squared value in determining how well the model explains the variability in housing prices.

A higher R-squared value indicates that a larger proportion of the variance in the dependent variable (housing prices) can be explained by the independent variables in the model. This suggests that the model captures more of the underlying relationships and patterns within the data, which is crucial for making reliable predictions. A model that is capable of explaining more variance is likely to provide more insight into how different factors impact housing prices, thus making it a strong candidate when comparing performance.

It is essential, however, to recognize that R-squared alone does not measure prediction accuracy. That’s where MAE comes into play. While a model with a lower MAE suggests better predictive accuracy, it is a different type of metric and does not directly convey how much variance the model is capturing. Therefore, when prioritizing models based on R-squared, the emphasis is on the model's explanatory power regarding variance.

In summary, the choice of the model with the higher R-squared value stems from its ability