Accuracy is always the primary metrics that is used to measure a model’s performance.

This statement is not entirely correct. While accuracy is an important metric, it is not always the primary one used to measure a model's performance. The choice of metric depends on the specific task or problem at hand. For instance, in cases where the data is imbalanced, precision, recall, or the F1 score might be more appropriate. Additionally, in regression tasks, metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE) could be used. Therefore, it's crucial to choose the right metric based on the context and the nature of the problem.