python - In multi-label classification, how do we find the P values and confidence interval for the performance evaluation metrics?

Question

I am doing a multilabel classification using two different classifiers with a dataset of 7 labels and 20 features. I have computed the accuracy, sensitivity, specificity, and area under the curve (AUC) metrics. Now, I want to report the P values and confidence intervals for AUC. I got this article, which is applicable for a binary classification problem. But, how can we implement this in multi-label settings?

Kindly provide some suggestions on this?

Thank you in advance!

Answer 1

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