Learn about the key concept of confidence as the calculated probability of a correct image classification in the context of the Microsoft Azure AI-900 certification exam. Understand how confidence differs from accuracy, sentiment analysis, and root mean square error.
Table of Contents
Question
Which of the provided options is considered the calculated probability of a correct image classification? Select the correct option.
A. Accuracy.
B. Confidence.
C. Sentiment.
D. Root Mean Square Error.
Answer
A. Accuracy.
Explanation
In the context of image classification using Azure Cognitive Services or custom vision models, confidence refers to the calculated probability that the model’s predicted classification for an image is correct.
When a trained image classification model evaluates an image, it outputs the class or label it predicts the image belongs to, along with a confidence score between 0 and 1. This confidence score represents the model’s estimated probability that its classification is accurate based on patterns it learned from training data. The higher the confidence score, the more certain the model is about the predicted class.
Confidence is not the same as these other terms:
- Accuracy measures the overall performance of a model – the fraction of predictions it gets right. But accuracy is measured on a test set, not calculated for individual predictions.
- Sentiment analysis refers to determining the emotional tone of language as positive, negative or neutral. It’s not directly related to image classification confidence.
- Root Mean Square Error (RMSE) is a metric for regression models that measures the typical magnitude of errors between predicted and actual values. It’s not used to assess classification probabilities.
So in summary, confidence, outputted as a probability for each prediction, specifically represents the model’s assessment of the likelihood it classified an image correctly. The other choices describe different machine learning concepts not directly synonymous with this calculated classification probability.
Accuracy is the metric that represents the calculated probability of a correct image classification, indicating the proportion of true results (both true positive and true negative) among the total number of cases examined.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.