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What is the confidence score in AI image classification?
Prepare for the AI-900 exam by understanding the difference between confidence and accuracy in image classification. Learn why the confidence score represents the calculated probability of a single correct prediction and how it’s used in Azure AI services.
Question
____________________ is the calculated probability of correct image classification.
A. Confidence
B. Sentiment
C. Accuracy
D. Root Mean Square Error
Answer
A. Confidence
Explanation
The correct term is A. Confidence. In machine learning, the confidence score represents the model’s calculated probability that a specific prediction is correct.
Understanding Confidence Score
When an image classification model analyzes an image, it doesn’t just output a single label; it calculates a probability for each possible class it was trained to recognize. The class with the highest probability is chosen as the prediction. That probability score is the confidence of the prediction. For example, if a model is shown a picture of a cat, it might calculate:
- Cat: 0.98
- Dog: 0.01
- Fox: 0.01
The model’s prediction would be “Cat,” and it would report this prediction with a confidence of 0.98, or 98%. This score is a measure of the model’s certainty for that single prediction.
Distinguishing Confidence from Accuracy
It is critical to distinguish confidence from accuracy.
- Confidence is a value associated with an individual prediction.
- C. Accuracy is a metric used to evaluate the overall performance of the model across an entire dataset. It is calculated by dividing the number of correct predictions by the total number of predictions. A model can have high accuracy overall but still make some individual predictions with low confidence.
Why Other Options Are Incorrect
B. Sentiment: This is a specific type of output from a natural language processing model that determines the emotional tone of text (e.g., positive, negative, neutral). It is not related to image classification probability.
D. Root Mean Square Error (RMSE): This is a performance metric for regression models, which predict continuous numerical values (like price or temperature). It measures the average error in the model’s predictions and is not used for classification tasks.
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