AI-900: How to calculate accuracy for machine learning models

Learn how to calculate accuracy, a metric that measures the ratio of correct predictions to the total number of predictions, for machine learning models. This is a common question in the AI-900 Exam, the Microsoft Azure AI Fundamentals certification exam.

Question

Which metric presents the ratio of correct predictions (true positives + true negatives) to the total number of predictions?

A. Recall
B. F1 Score
C. Precision
D. Accuracy

Answer

D. Accuracy

Explanation

Accuracy presents the ratio of correct predictions (true positives + true negatives) to the total number of predictions.

The correct answer is D. Accuracy. Accuracy is a metric that measures how well a machine learning model can correctly predict the true labels of a given dataset. Accuracy is calculated by dividing the number of correct predictions (true positives + true negatives) by the total number of predictions (true positives + true negatives + false positives + false negatives). Accuracy is a simple and intuitive way to evaluate the performance of a binary or multiclass classification model, but it has some limitations. For example, accuracy can be misleading when the dataset is imbalanced, meaning that one class is much more frequent than the others. In such cases, accuracy might not reflect the true quality of the model, and other metrics such as precision, recall, or F1 score might be more appropriate.

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.