Jan 14, 2015 · AUC is used most of the time to mean AUROC, which is a bad practice since as Marc Claesen pointed out AUC is ambiguous (could be any curve) while AUROC is not. Interpreting the AUROC The AUROC has several equivalent interpretations :

Jan 4, 2018 · In fact a perfect classifier would be at $(0,1)$. But yes, a curve passing through $(0.2,0.8)$ is likely to also have a high AUC. AUC is the area under the entire curve, not just a single point. This allows you to compare to models that model probability, not two classifiers. The choice of threshold gets made later and depends on your application.

Dec 31, 2024 · When I got into Machine Learning over 15 years ago, I learned that AUC stands for "Area Under the Curve", meaning "area under the ROC curve" and ROC being the "Receiver Operating Characteristic". Now I'm supervising students myself and (of course) they sometimes use different terms depending on what literature and sources they read first.

Aug 16, 2020 · That being said, you want to achieve as high an AUC value as possible. In cases where you get an AUC of 1, your model is essentially a perfect predictor for your outcome. In cases of 0.5, your model is not really valuable. An AUC of 0.5 just means the model is just randomly predicting the outcome no better than a monkey would do (in theory).

Sep 7, 2022 · AUC is a simplified performance measure. AUC collapses the ROC curve into a single number. Because of that a comparison of two ROC curves based on AUC might miss out on particular details that are left out in the transformation of the ROC curve into the single number. So a higher AUC does not mean a uniform better performance.

May 10, 2019 · A perfect predictor gives an AUC-ROC score of 1, a predictor which makes random guesses has an AUC-ROC score of 0.5. If you get a score of 0 that means the classifier is perfectly incorrect, it is predicting the incorrect choice 100% of the time.

Mar 24, 2015 · 1.The first is the usual Area Under the Curve (AUC) of the Receiver Operator Characteristic. As explained in the comments, AUC ranges from 0.5 to 1, with 1 being perfect classification and 0.5 being no better than luck. Since there are only two outcomes, the algorithm can either classify positive correctly, or incorrectly.

Oct 13, 2021 · AUC is difficult to understand and interpret even with statistical knowledge. Without such knowledge I'd stick to the following stylized facts: AUC close to 0.5 means a model performance wasn't better than randomly classifying subjects. It wasn't better than a silly random number generator to mark the samples as positive and negative.

You are comparing the best overall accuracy and AUC. But they are still different concept again. The AUC is the P(predicted TRUE|actual TRUE) vs P(FALSE|FALSE), while the overall accuracy is the P=P(TRUE|TRUE)*P(actual TRUE) + P(FALSE|FALSE)*P(actual FALSE). So this depends on the proportion of the true value on your data set very much.

I found there are two ways to understand what AUC stands for but I couldn't get why these two interpretations are equivalent mathematically. In the first interpretation, AUC is the area under the ROC curve. Picking points from 0 to 1 as threshold and …