Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Which metric is considered the primary measure of a model's performance?

1. Accuracy

2. Precision

3. Recall

4. F1 Score

The correct answer is: Accuracy

Accuracy is often regarded as the primary measure of a model's performance because it represents the proportion of correctly predicted instances out of the total instances evaluated. It gives a straightforward indication of how well the model is performing in general terms. When a model is evaluated, calculating accuracy provides a high-level understanding of its effectiveness across all classes in a balanced dataset. However, while accuracy is useful, it's important to note that in certain situations, especially with imbalanced datasets, relying solely on accuracy may not give a complete picture of a model's performance. Metrics like precision, recall, and F1 score can provide more nuanced insights, particularly when the costs of false positives and false negatives are significant. In summary, accuracy serves as a fundamental and commonly used metric to assess model performance, but it may need to be complemented by other metrics depending on the specific context and goals of the model evaluation. This underscores the importance of considering the broader context in which model performance is evaluated.