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Can you have high sensitivity and low specificity?
In general, high sensitivity tests have low specificity. In other words, they are good for catching actual cases of the disease but they also come with a fairly high rate of false positives. Mammograms are an example of a test that generally has a high sensitivity (about 70-80%) and low specificity.
How does sensitivity and specificity change with prevalence?
Overall, specificity was lower in studies with higher prevalence. We found an association more often with specificity than with sensitivity, implying that differences in prevalence mainly represent changes in the spectrum of people without the disease of interest.
What is better sensitivity or specificity?
A highly sensitive test means that there are few false negative results, and thus fewer cases of disease are missed. The specificity of a test is its ability to designate an individual who does not have a disease as negative. A highly specific test means that there are few false positive results.
What does low sensitivity high specificity mean?
Likewise, high specificity — when a test does a good job of ruling out people who don’t have the disease – usually means that the test has lower sensitivity (more false-negatives).
When is AUC is likely to be high?
If that is pretty much the definition, then would that mean if your sensitivity and specificity are high (e.g. both are higher than say 80%) then AUC is likely to be high also? Let’s say I’m building a model that’s trying to classify whether something is a cat or dog. Could someone explain the concept of AUC and relate it to this example?
Why are there different thresholds for AUC ROC curve?
Setting different thresholds for classifying positive class for data points will inadvertently change the Sensitivity and Specificity of the model. And one of these thresholds will probably give a better result than the others, depending on whether we are aiming to lower the number of False Negatives or False Positives.
Which is better point C or D in AUC curve?
Between points C and D, the Sensitivity at point C is higher than point D for the same Specificity. This means, for the same number of incorrectly classified Negative class points, the classifier predicted a higher number of Positive class points. Therefore, the threshold at point C is better than point D.
Where is the sensitivity of the ROC curve?
It would be on the top-left corner of the ROC graph corresponding to the coordinate (0, 1) in the cartesian plane. It is here that both, the Sensitivity and Specificity, would be the highest and the classifier would correctly classify all the Positive and Negative class points.