Metrics & Evaluation in Retrieval & Knowledge-Driven AI

Introduction Key Concepts Metrics Evaluation Process Best Practices FAQ

1. Introduction

Metrics and evaluation are crucial in assessing the performance of retrieval systems and knowledge-driven AI models. Understanding how to measure effectiveness allows developers and researchers to improve these systems iteratively.

2. Key Concepts

Definitions

Precision: The ratio of relevant instances retrieved to the total instances retrieved.
Recall: The ratio of relevant instances retrieved to the total relevant instances available.
F1 Score: The harmonic mean of precision and recall, providing a balance between them.

3. Metrics

Key metrics for evaluating retrieval and knowledge-driven systems include:

Precision
Recall
F1 Score
Mean Average Precision (MAP)
Normalized Discounted Cumulative Gain (NDCG)

4. Evaluation Process

The evaluation process typically follows these steps:


graph TD;
    A[Define Evaluation Goals] --> B[Select Metrics];
    B --> C[Collect Data];
    C --> D[Compute Metrics];
    D --> E[Analyze Results];
    E --> F[Iterate on Model];
    F --> A;

5. Best Practices

Ensure to define clear evaluation goals and select metrics aligned with those goals.

Utilize a diverse dataset for evaluation.
Benchmark against established models.
Iterate and refine based on feedback.
Document all evaluation processes and results.

6. FAQ

What is the difference between precision and recall?

Precision measures the accuracy of the retrieved instances, while recall measures the model's ability to retrieve all relevant instances.

What is F1 Score used for?

The F1 Score provides a single metric that balances both precision and recall, especially useful when you need to find an optimal balance between the two.

Why use NDCG?

NDCG is useful for evaluating ranked results, as it accounts for the position of relevant documents in the result set.