Can accuracy be lower than AUC?
So how can it be that the AUC is large while the accuracy is low at the same time? Well this may happen if 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 negative).
Is high or low AUC better?
The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.
How is AUC ROC curve related to classification accuracy?
Besides Classification Accuracy, other related popular model performance measures are sensitivity, specificity, precision, recall, and auc-roc curve. To calculate the classification accuracy, you have to predict the class using the machine learning model and compare it with the actual class.
What is the difference between ROC AUC and PR AUC?
Similarly to ROC AUC score you can calculate the Area Under the Precision-Recall Curve to get one number that describes model performance. You can also think of PR AUC as the average of precision scores calculated for each recall threshold.
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.
How is the auprc related to the PR curve?
You can always report the AUPRC and AUROC together. The AUPRC is calculated as the area under the PR curve. A PR curve shows the trade-off between precision and recall across different decision thresholds. (Note that “recall” is another name for the true positive rate (TPR). Thus, AUPRC and AUROC both make use of the TPR.