Using “accuracy” as a measure of performance for logistic regression

I can't quite wrap my head around something that should be relatively fundamental. I'm familiar with ROC curves and AUC, but what I'm confused about is using accuracy for logistic regression. My understanding is that a logistic regression model outputs probabilities, and then those probabilities can be classified as one of either class (assuming a binary dependent variable), based on a threshold value. This makes sense in the case of an AUC value, since the value represents the entire range of the threshold from 0 to 1. But how can we use "accuracy" as a way to determine the performance of a model when the predicted classes are dependent on the threshold value? I keep seeing it used in examples of Python code (using sklearn) but don't understand how it works. Using AUC make sense, but accuracy I don't get.

Shouldn't we get a whole range of accuracy values for each model, the same way we get a whole range of true positive and false positive rates for each model that combine to give a single AUC score?

• Its a bad practice and you are correct to be suspicious. Many of these sources indiscriminately apply a threshold of 0.5, which has many issues. – Matthew Drury May 18 '17 at 0:04
• Why not simply call it as you see it and refer to the AUC as the AUC? "Accuracy" is too ambiguous: technically a non-deterministic model is inaccurate since it can't predict the outcome correctly with probability 1. If anything: the AUC is a measure of recall, if you use its proper definition. – AdamO Jun 2 '17 at 15:46