I'm working on a project to predict bots from legit users from social medias. The data that I collected has about 5% of bots for 95% of legit users. The problem is as I labelled my data, I was more looking to label bots rather than legit users as it's easier to find bots (they mostly have the same messages, bio, photos, bio URL domain, etc). Labelling real people his very hard though, and I didn't find a good way to label them with certainty except manually, one by one.
Totally, there are 140k rows of data. I labelled about 35k, 20% are bots, not the same as 5%. Is that a big issue?
I used Randomforest to make a model that got me .87+ for accuracy, precision, recall, auc and MCC. Is it okay to not have the same distribution? What should I do?