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How exactly does 14 "correct" predictions, 8 false positives, and 1 false negative equate to 70% accuracy?
by bhaney 3y ago
How exactly does 14 "correct" predictions, 8 false positives, and 1 false negative equate to 70% accuracy?
- beezle 3y agoThe AI told them that % and nobody questioned it :)
- hyperhopper 3y ago~61% is what that sounds like. Even lower it you take the f1 score...
- gpm 3y agoI think it sounds more like 93% (14 predicted/(14 + 1) total earthquakes). False positives aren't failures to predict an earthquake, they're a different error with a different consequences. Of course, I'm not sure why I would trust the absolute numbers reported are more accurate than the percentages reported...
- bhaney 3y agoSounds good. In that case, I just made a 100% accurate model by always predicting that every location on Earth is experiencing an earthquake all the time.
- HankB99 3y agoEasy. 14÷(14+1+8) = 0.608695652 Oops. Maybe they didn't include the missed prediction. 14÷(14+8) = 0.636363636 Maybe they rounded up. A lot.
- mcpackieh 3y agoIt's a simple matter of cascading your rounding operations. 0.636 rounds up to 0.64 of course. That four is almost a 5 so round that up to 0.65. Everybody knows that it's fair to round up from a 5 so this is how they 0.7, but these amateurs forgot to subsequently round 0.7 up to 1. Shameful, really.
- jcranmer 3y agoThe article is so information-lite that a possibility I wouldn't rule out was that of 20 earthquakes in the trial, it made predictions for 14 earthquakes that were close enough to count as a success criterion, it made predictions for 5 earthquakes that were substantially off in location and/or magnitude but not entirely misses, 1 earthquake that it missed entirely, and 8 more earthquakes that it predicted that didn't occur.
- quickthrower2 3y agoShould be 14/15 = 93.3% Because: “of earthquakes”