3 ms·

The 90% is a misquote, good catch. The actual accuracy is between 60-90%, depending on clusters of symptoms, and apparently that was stripped somewhere during

• andrewljohnson 16y ago
  With a range that large, I would be highly suspect that when you did get 90%, it was just fitting the data. And how would you even confirm if they had the flu - even a doctor could misdiagnose mild flu, which adds more noise. 60% really means very little if it's a binary choice - flu or no. You'll be right 50% of the times no mater what.
  • moultano 16y ago
    >60% really means very little if it's a binary choice - flu or no. You'll be right 50% of the times no mater what. What? Just because something is binary doesn't mean it happens half the time.
    • andrewljohnson 16y ago
      Hmm, yeah... good point on that. I'll leave my comment, but I take it back :)
      • anmol 16y ago
        With severely unbalanced classes, overall accuracy is not a good metric. So we use recall of the symptom class, and how it varies with the precision. Recall = 0.6 to 0.9. All results are based on cross-validation. There is a lot more sophistication we can add (e.g. markov properties), these are almost first-order results. Nonetheless important to validate with other populations. That would be a big part of testing any product.