4 ms·

What's the False Positive Rate?

• ska 7y ago
  FP = system (or person) flags this as true when it is not TP = ... flags as true and it is FN = ... flags as false but it is true TN = ... flags as false and it is false To turn these into rates, you normalize them. e.g. TPR = TP/P = TP/(TP + FN) = 1 - FNR etc. These are characteristics of a classification system You will also hear sensitivity (TPR) vs. specificity (TNR) often, particularly in medical contexts. In other contexts you'll hear Type I (FP) vs. Type II (FN) error. In most cases you a set of trade offs in your algorithm, and will need to pick a balance between sensitivity and specificity. c.f. ROC: https://en.wikipedia.org/wiki/Receiver_operating_characteristic https://en.wikipedia.org/wiki/Receiver_operating_characteris...
  • adyavanapalli 7y ago
    I think the OP is asking about the _value_ of the FPR instead of the definition.
    • RosanaAnaDana 7y ago
      The value of the false positive rate is that it lets you know the probability of a true-miss. Depending on the classification exercise, you may be concerned with false positives, where the consequence of a missed call is significantly greater than an unwarranted checkup from a human doctor.
      • shawnz 7y ago
        The numeric value
    • ska 7y ago
      Ah, if I misread then from the results section of the linked paper: For the malignancy prediction objective, the algorithm obtained an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.93), with specificity of 77.3% (95% CI: 69.2%, 85.4%) at a sensitivity of 87%. I haven't read the papers methods, but the data set size is small-ish for this sort of analysis.
• TuringNYC 7y ago
  from the paper: https://pubs.rsna.org/doi/10.1148/radiol.2019182622 https://pubs.rsna.org/doi/10.1148/radiol.2019182622 AUC is 0.78 Sensitivity-Specificity Graph is here: http://images.rsna.org/index.html?doi=10.1148/radiol.2019182622&fig=fig3 http://images.rsna.org/index.html?doi=10.1148/radiol.2019182...
  • mdorazio 7y ago
    Thanks for linking. If I'm reading that correctly, it's pretty bad in comparison to an average human radiologist at ~6% false positive rate [1]. There's probably a bias factor in there, however, where humans are hesitant to predict potential cancer due to the cost/time involved in follow-up screening. [1] https://www.ncbi.nlm.nih.gov/pubmed/21643887 https://www.ncbi.nlm.nih.gov/pubmed/21643887