Exam AI-102 topic 2 question 31 discussion

1. 0 2. 25 https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/custom-text-classification/concepts/evaluation-metrics - Precision: Measures how precise/accurate your model is. It's the ratio between the correctly identified positives (true positives) and all identified positives. The precision metric reveals how many of the predicted classes are correctly labeled. Precision = #True_Positive / (#True_Positive + #False_Positive) - Recall: Measures the model's ability to predict actual positive classes. It's the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct. Recall = #True_Positive / (#True_Positive + #False_Negatives)

Asked in 28/06/2023 exam

The percentage of false positives is 0 because the precision of the model is 100%, which indicates that there are no false positives. Precision is calculated as the number of true positives divided by the sum of true positives and false positives. A precision of 100% means that all detected objects were correct, implying there were no false positives. The value for the number of true positives by the total number of true positives and false negatives is 25 because recall is calculated as the number of true positives divided by the sum of true positives and false negatives. The recall in the graphic is 25%, which indicates that the true positives are 25% of the total true positives and false negatives.

got this in Oct 2024 exam

0 & 25

1. 0 2. 25

1. 0 2. 25

The answer is correct. See https://learn.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/get-started-build-detector

It is true. #1:Precision = 100% #2:recall = 25%