Microsoft Certified Azure AI Engineer Associate - AI-102
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You have trained a custom image classification model using Azure AI Vision and evaluated its performance using various metrics. You find that the model's precision is high, but its recall is low. What does this indicate about the model's performance?
You have trained a custom image classification model using Azure AI Vision and evaluated its performance using various metrics. You find that the model's precision is high, but its recall is low. What does this indicate about the model's performance?
Explanation:
High precision indicates that the model is correctly classifying a high proportion of positive instances. However, low recall indicates that the model is missing some positive instances. This suggests that the model is not capturing all the positive instances in the data, leading to a lower recall. It does not necessarily indicate overfitting or underfitting, nor does it imply a high false positive rate. To improve the model's recall, you may need to consider techniques such as re-sampling the data, adjusting class weights, or using a different model architecture.