Evaluation Metrics are standards or criteria used to measure the performance and accuracy of AI models. Common metrics include retrieval score, response score, accuracy, precision, and recall. These metrics help in assessing how well an AI model performs its intended tasks.
- Glossary > Letter: E
What does "Evaluation Metrics" mean?

Use Cases
Model Testing:
Assessing the performance of different AI models to choose the best one for a specific application.
Quality Assurance:
Ensuring that AI systems meet predefined standards of accuracy and relevance.
Continuous Improvement:
Using metrics to identify areas for improvement and optimize AI models over time.

Importance
Objectivite:
Provides an objective way to measure and compare the performance of AI models.
Quality Control:
Ensures that AI systems deliver high-quality results.
Optimization:
Helps in identifying weaknesses and areas for improvement, leading to better-performing AI systems.

Analogies
Evaluation metrics are like a report card for AI models, providing a clear indication of how well they are performing and where they can improve.
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