What is AUC 1 indicates in the context of distinguish between positive and negatives?
The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes. When AUC = 1, then the classifier is able to perfectly distinguish between all the Positive and the Negative class points correctly.
What is better AUC or accuracy?
You have to choose one. For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set. AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else.
How to interpret almost perfect accuracy and AUC-ROC?
I am training ML logistic classifier to classify two classes using python scikit-learn. They are in an extremely imbalanced data (about 14300:1). I’m getting almost 100% accuracy and ROC-AUC, but 0% in precision, recall, and f1 score.
Why is rocauc set to false in Yellowbrick v0.9?
This has been fixed as of v0.9, where the micro, macro, and per-class parameters of ROCAUC are set to False for such classifiers. Yellowbrick’s ROCAUC Visualizer does allow for plotting multiclass classification curves.
Which is better PR AUC or F1 score?
PR AUC and F1 Score are very robust evaluation metrics that work great for many classification problems but from my experience more commonly used metrics are Accuracy and ROC AUC. Are they better? Not really. As with the famous “AUC vs Accuracy” discussion: there are real benefits to using both.
What’s the difference between AUC and ROC curve?
AUC is the area under ROC curve between (0,0) and (1,1) which can be calculated using integral calculus. AUC basically aggregates the performance of the model at all threshold values. The best possible value of AUC is 1 which indicates a perfect classifier.