I have data set which is highly unbalanced - target attribute is 93% False and 7% True. But I know that this is normal for my kind of data.
I am afraid that if I undertake any steps (I can take less False cases for example), I skew the distribution - the model will see True class as more frequent and give it higher probability, which in reality is not true.
Can I say that my dataset is unbalanced even if it represents reality? Or we talk about unbalanced data only if we took bad sample and in reality the data are balanced?
(I am asking because I am estimating the target attribute and I am considering if I should do something with my dataset regarding the unbalanced nature of it)