Sep 9, 2021 · There is no specific threshold for what is considered a good AUC score. Obviously the higher the AUC score, the better the model is able to classify observations into classes. And we know that a model with an AUC score of 0.5 is no better than a …

Jul 31, 2018 · One possible reason you can get high AUROC with what some might consider a mediocre prediction is if you have imbalanced data (in favor of the "zero" prediction), high recall, and low precision.

Jul 15, 2016 · The described situation with high AUC and low accuracy can occur when your classifier achieves the good performance on the positive class (high AUC), at the cost of a high false negatives rate (or a low number of true negatives).

Jul 6, 2018 · The more intuitive meaning of having a high ROC AUC, but a low Precision-Recall AUC is that your model can order very well your data (almost of of them belong to the same class anyway), but high scores do not correlate well with being positive class.

Sep 7, 2022 · Is the model with the higher AUC value always better? Not necessarily. If sensitivity is more important than specificity for your problem, a model with lower AUC can still be better. If calibration is important to you, then AUC will not help find a well-calibrated model.

Sep 13, 2020 · AUC stands for area under the (ROC) curve. Generally, the higher the AUC score, the better a classifier performs for the given task. Figure 2 shows that for a classifier with no predictive power (i.e., random guessing), AUC = 0.5, and for a perfect classifier, AUC = 1.0.

In the field of pharmacokinetics, the area under the curve (AUC) is the definite integral of the concentration of a drug in blood plasma as a function of time (this can be done using liquid chromatography–mass spectrometry [1]).

Jan 10, 2016 · One must understand crucial difference between AUC ROC and "point-wise" metrics like accuracy/precision etc. ROC is a function of a threshold. Given a model (classifier) that outputs the probability of belonging to each class, we predict the class that has the highest probability (support).

Sep 2, 2023 · A more balanced and robust model is one that achieves a reasonably high AUC score while also allowing for some level of uncertainty in its predictions. Let’s look at some benefits of using AUC score.

In pharmacology, the area under the plot of plasma concentration of a drug versus time after dosage (called “area under the curve” or AUC) gives insight into the extent of exposure to a drug and its clearance rate from the body.