#StackBounty: #classification #multiclass-classification #supervised-learning How to deal with broad and narrow variance within classes…

Bounty: 50

Let’s say I’m doing an animal image classification task (it doesn’t have to be image classification – this is just my example), and the training and test data is balanced across classes. The classes might be ['gorilla', 'giraffe', 'dog', 'donkey']. Now we all know that there is relatively a lot of variance within the 'dog' class compared to the other three classes.

So, is there any way one would treat this problem vs another problem where all classes have about the same amount of variance (where I might replace 'dog' with 'sheep' for instance)?

Get this bounty!!!

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