What are the limitations of AUC in evaluating models?
An example of an AI chatbot conversation about the limitations of AUC in model evaluation.
What are the limitations of AUC in evaluating models?
AUC, or Area Under the Curve, is a popular metric for evaluating classification models, but it has some limitations.
Can you explain some specific limitations?
Sure! One limitation is that AUC does not account for the actual distribution of classes in the dataset.
What does that mean for model evaluation?
It means that a model could have a high AUC but still perform poorly in real-world scenarios if the class distribution is imbalanced.
Are there any other metrics I should consider?
Yes, consider metrics like precision, recall, F1-score, and confusion matrix for a more comprehensive evaluation.
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