Timeline for How to compare class distributions between different datasets?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Oct 26, 2022 at 14:37 | history | edited | user1696420 | CC BY-SA 4.0 |
added 348 characters in body
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| Oct 26, 2022 at 14:18 | comment | added | dipetkov | So you want to pre-determine whether the additional datasets bring something or not, just based on high-level summaries. I think this is an important detail omitted from the body of your question. I would add it if I were you. | |
| Oct 26, 2022 at 14:04 | comment | added | user1696420 | @dipetkov some datasets are not public domain and we are trying to see if adding dataset B adds anything to A (and training is very costly so would like to have some proxy way of at least guessing about it) | |
| Oct 26, 2022 at 12:32 | comment | added | dipetkov | Why not combine the datasets into one? | |
| Oct 26, 2022 at 11:41 | comment | added | user1696420 | @StephanKolassa we are enforcing balance to remove/mitigate biases present in dataset. the aim is to construct an artificial dataset. we don't have a ground truth reference, since the task is generative and there are no "labels" of truth. if my goal was to identify high number of samples per class better, while penalizing sparsity amongst classes, what would be a good canonical way of visualizing this? | |
| Oct 26, 2022 at 11:08 | comment | added | Stephan Kolassa | "More balanced" is not better, especially not if there is a question of a dataset being representative of a population. "More representative" begs the question "more representative of what?, so you would need to compare your datasets to some ground truth or some dataset of known "quality". | |
| Oct 26, 2022 at 10:32 | history | asked | user1696420 | CC BY-SA 4.0 |