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What is a bad AUC score?
The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier.
What is considered a good AUC score?
The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6.
What does a good AUC look like?
An excellent model has AUC near to the 1 which means it has a good measure of separability. A poor model has an AUC near 0 which means it has the worst measure of separability. In fact, it means it is reciprocating the result. And when AUC is 0.5, it means the model has no class separation capacity whatsoever.
How do you read an AUC value?
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 to calculate the ROC AUC scores of different binary classifiers?
Below is a minimal example in scikit-learn which trains three different models on a binary classification dataset, plots the ROC curves and calculates the AUC scores. How to calculate the 95% confidence interval (CI) of the ROC AUC scores on the test set? (e.g. with bootstrapping).
When to use AUC-ROC curve in classification?
Like I said before, the AUC-ROC curve is only for binary classification problems. But we can extend it to multiclass classification problems by using the One vs All technique. So, if we have three classes 0, 1, and 2, the ROC for class 0 will be generated as classifying 0 against not 0, i.e. 1 and 2.
How to compute area under the ROC AUC?
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. Note: this implementation can be used with binary, multiclass and multilabel classification, but some restrictions apply (see Parameters). Read more in the User Guide.
How to compare ROC AUC scores of neural nets?
Where diff denotes the difference in roc curves between the two classifiers and p (diff>) is the empirical probability to observe a larger difference on a shuffled data set. One can use the code given below to compute the AUC and asymptotic normally distributed confidence interval for Neural Nets.