Data Imbalance: what would be an ideal number(ratio) of newly added class's data? Assume that I have 10 classes with 100 samples for each class—same # of samples, perfect balanced dataset.
I want to add 3 new classes, and which of the following is the best option for the number of samples for each newly added class?

*

*100, 100, 100

*200, 200, 200

*1000, 1000, 1000

*100, 1000, 1000

I am creating a data analysis product for my clients, and I have to make a threshold for the minimum (or maximum) number of samples they have to add.
It depends on the datasets we are using, but balanced data is almost always better than imbalanced.
However, I am pretty not sure how I can set the threshold for the newly added class' data sample number if I can choose it.

More data per group is always better than less data. It doesn't matter what sample sizes are off other groups.
The imbalance "problem" means that if you can collect only 1000 data points, it's usually better to have 500:500 than 100:900, but 100:900 will be better than 100:100 simply because there is more information on the data. It doesn't matter what's the balance. So an additional value of a data point is lower if you already have many data points from that class, but their value is never negative.
Some models and measures do have problems with unbalanced data, but that's just a modeling and competence issue, others are completely fine. There are many threads about it already on this site, but you are still better off collecting more data than less.
