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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
by 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 agoI think the OP is asking about the _value_ of the FPR instead of the definition.
- RosanaAnaDana 7y agoThe 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 agoThe numeric value
- ska 7y agoAh, 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.