Struggling with model evaluation in Azure AI? Discover why Precision, Recall, and F1 Score are critical metrics for image classification (Custom Vision) in Microsoft’s AI-900 certification exam. Master key concepts with real exam practice questions.
Table of Contents
Question
You are an Azure AI developer for Nutex Inc. You are developing an image recognition app and building an image classification model using the Custom Vision web portal. You will integrate the image classification model into the image recognition app.
You have trained the classifier and now want to evaluate the classifier model. Which of the following are the measurement metrics that can help you evaluate the effectiveness of the classifier model? (Choose all that apply.)
A. Probability
B. Availability
C. Precision
D. Recall
E. F1 Score
Answer
C. Precision
D. Recall
Explanation
Precision and Recall are measurement metrics that will help you evaluate the effectiveness of the classifier model. Precision measures the proportion of correctly predicted positive observations to the total number of predicted positives. High precision indicates a low false-positive rate, which is important in applications where minimizing incorrect positive predictions is critical. Precision is crucial for applications where the cost of false positives is high, making it a key metric for evaluating the classifier model.
Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified. High recall indicates that the model is good at detecting positive cases. Recall is important when the cost of missing positive instances is high, making it another critical metric for model evaluation.
Probability is not a measurement metric for evaluating the effectiveness of the classifier model. Probability is a score or confidence level that the model assigns to each prediction. It gives insight into the model’s confidence but is not an evaluation metric on its own.
The F1 Score is not a measurement metric for evaluating the effectiveness of the classifier model. The F1 Score is a mean calculation of precision and recall and provides the balanced evaluation of a model’s performance, which is particularly useful in scenarios with imbalanced classes. It is a combination of both false positives and false negatives, making it a useful metric when you need to balance precision and recall.
Availability is not a measurement metric for evaluating the effectiveness of the classifier model. Availability refers to system uptime or the reliability of accessing services and is not a metric used for evaluating the performance of machine learning models. It is important for assessing system performance, but it does not measure how well a machine-learning model classifies images.
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