Your assumption is right, in terms of business cost, we'd rather see a false positive than a false negative.

I don't think the scikit learn algorithm differentiates between the two types of errors, in terms of cost.

Though it seems to give less false negatives than false positives overall, when testing on new datasets.

I'll put in the f1 score in the article when I have time.