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What are thresholds in AUC?
1. 0. AUC curve is generated based on TPR/FPR of different thresholds. The main point of ROC is to sample threshold from (0;1) and get a point for curve. Notice that if your classifier is perfect you will get point (0,1) and for all smaller threshold cant be worst, so it also will be on (0,1) which leads to auc = 1.
What is a significant AUC value?
In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.
How do you interpret AUC results?
AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example. AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.
How are thresholds used in the AUC score?
Say using sklearn.metrics.roc_auc_score you pass y_true and y_rfc (being the true value and the predicted value), but I do not see how the thresholds come into play in the AUC score/plot. I have read different guides/tutorials for AUC, but all of their explanation regarding the threshold and how it is used is kinda vague.
How does sklearn actually calculate AUC ( area under curve )?
I have also had a look at How does sklearn actually calculate AUROC? . AUC curve is generated based on TPR/FPR of different thresholds. The main point of ROC is to sample threshold from (0;1) and get a point for curve.
What’s the difference between ROC and AUC curves?
AUC – ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. It tells how much model is capable of distinguishing between classes. Higher the AUC, better the model is at predicting 0s as 0s and 1s as 1s.
Is the AUC value of a model significant?
By itself, it does not have any significance. Whereas — say you have the prediction results from 2 models — one with the value of 0.96 and other with 0.88, then you could determine the model having higher AUC is better for your data. In Machine Learning, performance measurement is an essential task.