Jan 14, 2015 · Thanks a lot!! Quick question- You said AUC less than 0.5 is good too as that means we can invert the model decision?. So are you saying if I have AUC =0.3, then if a model is predicting an instance (x vector) as positive label (1) then I …

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).

Jan 10, 2019 · When reading this article, I noticed that the legend in Figure 3 gives a p-value for each AUC (Area Under the Curve) from the ROC (Receiver Operator Characteristic) curves. It says: It says: The area under the curve (AUC) is 1.0 (p < .001) for the overall D-IRAP scores, 0.95 (p < .001) for the female picture bias scores and 0.94 (p < .001) for ...

The AUC is equal to the probability that a randomly sampled positive observation has a predicted probability (of being positive) greater than a randomly sampled negative observation. You can use this to calculate the AUC quite easily in any programming language by going through all the pairwise combinations of positive and negative observations.

Feb 27, 2025 · When we try to determine the optimal threshold for a continuous predictor variable, we draw a ROC curve and calculate the AUC value. If AUC<0.5 this means that the predictor has an inverse relationship with the outcome. Is that correct? If yes, may this result in very high levels of cut off / threshold values, since this test provides the value ...

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.

Aug 26, 2014 · However, there is definitely value in understanding that a 0.95 AUC-ROC, for example, means that you have essentially solved the problem and have a very, very good classifier. Whereas an AUC of 0.6 for finding profitable investments might be, strictly speaking, better than random, but not much better.

May 10, 2019 · Overfitting can only lead to the AUC on the validation data being closer to the random model, but not going under. I'm sure it can be statistically proven that if test and validation sets are truly random and have the same distribution, it wouldn't be possible for the validation AUC to be lower than 0.5 other than by chance. $\endgroup$ –

Aug 26, 2016 · THE CLASSICAL AUC CALCULATION WOULD USE THE PROBABILITIES. When people report model AUC values, the typical approach would be to use the probability values or some strictly increasing function of those probability values (e.g., the log-odds from a logistic regression), as strictly increasing functions do not change the AUC.

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.