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I'm trying to create a Random Forest Classifier for selecting ~ 700 features.

I have a highly imbalanced dataset to select features from. There are significantly fewer positive cases (1%) compared with negative cases.

I'm trying to select an appropriate evaluation metric between the following options; F1, PR-AUC and balanced accuracy. Can anyone give some insight as to which is the most suitable?

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