Accuracy is always the primary metric used to measure a model's performance.
The correct answer is False.
Accuracy is not always the primary metric used to measure a model's performance. While it is important, other metrics such as precision, recall, F1 score, or area under the ROC curve (AUC) may be more relevant, especially in cases of imbalanced datasets or specific use cases where false positives or false negatives have different consequences. The choice of performance metric depends on the problem being solved.
Accuracy is not always the primary metric used to measure a model's performance. While it is important, other metrics such as precision, recall, F1 score, or area under the ROC curve (AUC) may be more relevant, especially in cases of imbalanced datasets or specific use cases where false positives or false negatives have different consequences. The choice of performance metric depends on the problem being solved.
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