Explanation:
Confidence.
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," the confidence score represents the calculated probability that a model's prediction is correct. In image classification, when an AI model analyzes an image and assigns it to a specific category, it also produces a confidence value-a numerical probability (usually between 0 and 1) indicating how certain the model is about its prediction.
For example, if an image classification model identifies an image as a "cat" with a confidence of 0.92, it means the model is 92% certain that the image depicts a cat. The confidence value helps developers and users understand the model's certainty level about its classification output.
Microsoft Learn emphasizes that in Azure Cognitive Services-such as the Custom Vision Service-each prediction result includes both the predicted label (class) and a confidence score. These confidence scores are essential for evaluating model performance and determining thresholds for automated decisions (e.g., accepting predictions only above a 0.8 probability).
Let's evaluate the other options:
* Accuracy: This is an overall performance metric measuring the percentage of correct predictions across the dataset, not a probability for a single prediction.
* Root Mean Square Error (RMSE): This is a metric for regression models, not classification tasks. It measures average error magnitude between predicted and actual values.
* Sentiment: This is a type of prediction (positive, negative, neutral) in text analysis, not a probability metric.
Therefore, based on Microsoft's AI-900 training materials and Azure Cognitive Services documentation, the calculated probability of a correct image classification is called Confidence, which expresses how sure the model is about its prediction for a specific input.