It is quite common to discuss what metrics to be used for a model/algorithm/method. But when it comes to metrics of datasets, it is much less discussed.
I wonder, what are the general metrics for datasets? That is to say, how does one quantitively measure if a dataset is good or not for its purpose?
I understand, there may not be too many general metrics, given the purpose and nature of datasets differ vastly. So, more specifically, I am interested in the follow few cases:
what are the metrics for 1) text datasets, 2) classification datasets, 3) unbalanced classification datasets?
Any knowledge, insights, interesting ideas are welcome and appreciated.