3 ms·
You are input to a classification routine. The job of those designing the routine is to reduce the false positive rate to as close to 0 as they can without incu
by ska 9y ago
You are input to a classification routine. The job of those designing the routine is to reduce the false positive rate to as close to 0 as they can without incurring too high a false negative rate.
So this sucks if you don't fit the model well in a way that has you often end up as a false negative - but that doesn't' mean the model is broken.
- kitanata 9y agoNo. The model has a problem with overfitting. That’s my point.
- ska 9y agoSaying it doesn't make it true. You are claiming there is a generalization problem that causes extra error in practice. Another perfectly viable hypothesis is that the classifier is working fine, it's just tuned for true positive rate and accepts a higher false negative rate to get it. Specificity vs. sensitivity is a fundamental trade off, not a training issue (though that can make both worse)