What metrics are used to evaluate the accuracy of Azure Computer Vision?

• A. Net promoter score
• B. Precision, recall, and F1-score
• C. Customer satisfaction index
• D. Return on investment

Answer: (B)

The accuracy of Azure Computer Vision is primarily evaluated using metrics such as precision, recall, and F1-score.

Precision measures the accuracy of the positive predictions made by the model, indicating how many of the predicted positive instances are actually positive. Recall, on the other hand, assesses the model’s ability to identify all relevant instances, reflecting how many actual positives were correctly predicted. The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns, particularly useful in scenarios where class distributions are imbalanced.

Utilizing these metrics allows developers and data scientists to gain insights into the performance of the computer vision model, making it easier to optimize and enhance its predictive capabilities based on real-world performance. This focus on precise evaluation helps in understanding how well the AI performs its intended tasks.

In contrast, options referring to net promoter score, customer satisfaction index, and return on investment relate more to business performance and customer experience rather than the direct evaluation of model accuracy. These metrics do not provide the necessary insights into the effectiveness of predictive algorithms used in computer vision tasks.