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Nov 20, 2018 at 5:41 comment added Siddhi Kiran Bajracharya One way to handle such imbalance classes is to downsample the majority class.
Nov 20, 2018 at 1:01 history bumped CommunityBot This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
Oct 20, 2018 at 5:00 history bumped CommunityBot This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
Sep 21, 2018 at 8:17 comment added Nicolas Raoul @Aditya: About the PS: real-world data proportions are different from proportions of the sample I have access to.
Sep 20, 2018 at 4:11 history edited Nicolas Raoul CC BY-SA 4.0
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Sep 20, 2018 at 3:49 answer added user12075 timeline score: 1
Sep 20, 2018 at 3:43 comment added Aditya I believe that you can go with 500 Vs 10000-30000 easily with augmentations, but that also depends how accurate you want the model to be, Are the images of the major class varying greatly(if yes, then the selection will be done selectively), what is it going to be used for, Also we really need to try oversampling for the minor lcass and under sampling for the major, create multiple copies of the minor class(simply duplicating will also help, via adding rotations(+-x degrees) of the image by a script(comes under augmentation but we can add it manually to improve count ).PS 500 isn't 5% of 1e6
Sep 20, 2018 at 3:30 comment added Nicolas Raoul @Aditya: As stated in the question, there is no more data for the first class. That is the premise of the question. You say 1,000,000 is too high, right? Do you suggest I randomly sub-sample? If yes, what number sounds right to you?
Sep 20, 2018 at 3:18 comment added Aditya The figure is too high! You need to get more data or use heavy augmentations.. Even then the model will be better predicting the major class ....
Sep 20, 2018 at 2:38 history asked Nicolas Raoul CC BY-SA 4.0