When comparing regression models for predicting future sales, which performance metric should be prioritized for reliability?

Prioritizing R-squared (Coefficient of Determination) as the performance metric when evaluating regression models for predicting future sales is sound because it provides insights into how well the model explains the variability of the dependent variable based on the independent variables. R-squared measures the proportion of variance in the target variable that can be predicted from the independent variables, with values ranging from 0 to 1. A higher R-squared value indicates a better fit of the model to the data, making it a valuable metric for understanding the reliability of the predictions.

This metric is especially useful in business contexts, like sales forecasting, where understanding the extent to which the predictors account for sales variation is crucial for decision-making. It helps compare different models effectively, as a model with a higher R-squared indicates that it captures the underlying trend of the data more accurately, which leads to more reliable sales predictions.

While Mean Squared Error (MSE) is also a valuable tool for assessing the accuracy of the predictions by evaluating the average squared differences between predicted and actual values, it does not provide a direct understanding of how much variance is explained by the model. Metrics like accuracy and cross-entropy loss are typically more relevant in classification contexts rather than regression, as they measure performance