[iptq]

I wish that reputable reporting on this kind of topic would start putting accuracy next to what it can reportedly do. I remember visiting a machine learning poster session where many of the posters reported results with accuracy as low as 30%.

If a program is able to predict this once or twice it's not a miracle. If it's able to do so with 60% I'd raise some eyebrows. But I'd say it's only a turning point when it's able to beat false positive rates of human doctors. Without an accuracy score, this news is absolutely meaningless.

[jncfhnb]

Accuracy is well understood to be a terrible, misleading metric and any reputable report would strive to avoid emphasizing its importance.

For those curious their test set AUC appears to be 77.

[monero-xmr]

Maybe I’m misunderstanding, but if you could have a test that doesn’t say anything if it isn’t sure, but if it is sure with very high confidence gives you a true positive, that has a lot of value.

[ShroudedNight]

As an aside, this was also the only strategy that seemed to scale in intrusion detection.

[r-zip]

Even with an accuracy score, it’s meaningless. Accuracy isn’t the right metric.

[glompers]

If you like, please mention more; I at least am interested to hear

[malfist]

Consider it this way.

If your AI algo scanned the general population and it said 100% of the time, "no risk of esophageal cancer" It'd still be 99.5% accurate.

It's highly accurate. And useless.

To get a good idea of how useful it is, you need to know it's false negative rate and false positive rate as well as it's accuracy.