Explore the significance of AUC values in performance metrics. Learn why an AUC score of 1 signifies a perfect classifier in model evaluation and assessment.
Table of Contents
Question
What is the ideal value for AUC?
A. 2
B. 0.5
C. 0
D. 0.1
E. 1.0
F. O.75
Answer
E. 1.0
Explanation
The ideal value for Area Under the Curve (AUC) typically ranges between 0 and 1, with 1 indicating a perfect classifier and 0.5 indicating a random classifier. Therefore, the ideal value for AUC would be:
E. 1.0: This represents a perfect classifier where the model predicts all positives perfectly and all negatives perfectly, resulting in an AUC score of 1.
Area Under Curve (AUC) is the model performance metrics for classification models. For binary classification models, the AUC value of 0.5 represents the random predictions: the model predictions are the same as randomly selected values of “Yes” or “No.” If the AUC value is below 0.5, the model performance is worse than random. Ideally, the best-fitted model has a value of 1. Such an ideal model predicts all the values correctly.
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