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Active and Sample-Efficient Model Evaluation
This is a great work! When I use it on my own dataset, it performs well when evaluating accuracy by using accuracy_loss. However, I'm wondering if it can evaluate precision or recall unbiasedly as well since in some class-unbalanced case, precision or recall is a more meaningful metric.
What I've done to evaluate the precision is, preactively updating the dataset.test_idx and dataset.test_remaining to refer to those samples which are predicted as positive by the model. Then I use the accuracy_loss to evaluate precision since they are identical when the testing samples are all predicted as positive by the model.
But no matter what dataset I use, the variance of active-testing is always close to i.i.d sampling despite the bias is still zero. I hope you can give me some ideas about this. Thank you.
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