One thing I came across is that 99% precision/recall/F1 score might not be enough. Especially in large sample sets "only" accurately identifying 99% leaves you

One thing I came across is that 99% precision/recall/F1 score might not be enough. Especially in large sample sets "only" accurately identifying 99% leaves you with too many wrong classifications. Eg 1.000.000 samples -> 10.000 false classifications. No idea if there is a different metric that somehow (again) takes the number of sample into account.