Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 96 discussion

The F1 score is a widely used metric for evaluating the accuracy of a model, especially in classification tasks where there is an imbalance between precision and recall. It is the harmonic mean of precision and recall, providing a balanced measure of the model’s ability to correctly identify relevant information while minimizing false positives and false negatives. In the context of a help desk model, you want to measure both the precision (correctness of answers) and recall (how well the model retrieves the relevant information). The F1 score helps you achieve a balanced view of these two metrics, making it a good choice for evaluating model accuracy in a fine-tuned large language model (LLM) for answering questions.

C: F1 score Explanation: The F1 score is a balanced metric that combines precision and recall to evaluate the accuracy of a model, particularly in scenarios like question-answering, where both correctness (precision) and completeness (recall) matter. The F1 score is particularly useful when there is an uneven distribution of classes or when the model's ability to retrieve relevant and accurate answers is being assessed.

The correct answer is C. F1 score combines precision and recall, making it ideal for question-answering evaluation.

The F1 score provides a balanced evaluation of the model's ability to give both relevant and accurate answers, making it the most suitable metric for assessing the fine-tuned model’s performance in answering help desk questions.

F1 score is a metric that combines precision and recall to evaluate the balance between correctly identified outputs and missed or irrelevant outputs. It is particularly useful for tasks like question answering, where both accuracy and completeness are critical. In this help desk scenario, the F1 score helps assess whether the model consistently provides correct and relevant answers to user queries, reflecting the effectiveness of fine-tuning.