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.

All else being equal, more data is always better. So #3 is clearly the best option.
Imbalanced data is not really a problem, and sacrificing more data for balance is throwing away free information (as Stephan Kolassa notes, the cost of data collection could be a concern - I am ignoring that for now).
See the following questions for more detailed discussion about this common misconception:
Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
When is unbalanced data really a problem in Machine Learning?
Does an unbalanced sample matter when doing logistic regression?
What is the root cause of the class imbalance problem?
This would be a more difficult choice if instead of [1000, 1000, 1000], option #3 was something like [10, 1000, 1000]. In that case, it is arguable whether you would learn enough about that one class from 10 samples to make the additional benefit of 1000 samples from the other 2 classes worth it - so [200, 200, 200] or [100, 1000, 1000] might be better options.
