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I have a multi-class image classification model and would like some metrics to evaluate its performance, including precision, recall, F1 score, mAP, and roc_auc score. However, based on the documentation, it seems that only roc_auc_score is supported. Can we treat each class as a binary case and calculate the F1 score , for example, for each class and then average them?
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Hi,
I have a multi-class image classification model and would like some metrics to evaluate its performance, including precision, recall, F1 score, mAP, and roc_auc score. However, based on the documentation, it seems that only roc_auc_score is supported. Can we treat each class as a binary case and calculate the F1 score , for example, for each class and then average them?
If the answer is no, can anyone explain why?
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