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What is a weighted AUC?
weighted AUC (wAUC), is a better way to measure the imbalanced data learning classifiers. When computing the weighted area under the ROC curve, weights vary with the values of the true positive rate (TPrate) among regions in a bid to focus on the accuracy of minority class that is more important in common.
What does area under ROC curve mean?
As the area under an ROC curve is a measure of the usefulness of a test in general, where a greater area means a more useful test, the areas under ROC curves are used to compare the usefulness of tests. The term ROC stands for Receiver Operating Characteristic.
Which is better weighted area under ROC or weighted AUC?
weighted AUC (wAUC), is a better way to measure the imbalanced data learning classifiers. When computing the weighted area under the ROC curve, weights vary with the values of the true positive rate (TPrate) among regions in a bid to focus on the accuracy of minority class that is more important in common.
What is the area under the ROC curve?
AUC (Area under the ROC Curve). AUC provides an aggregate measure of performance across all possible classification thresholds. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example.
What’s the difference between ROC and area under PR?
Figure 3 contains ROC and PR curves for identical models, clearly showing the difference between the two. To compare area under the PR versus area under ROC you can compare tables 1-2 (AUPR) and tables 3-4 (AUROC) where you can see that AUPR shows much larger differences between individual models than AUROC.
What does ROC curve mean in machine learning?
ROC curve. An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate; False Positive Rate; True Positive Rate (TPR) is a synonym for recall and is therefore defined as follows: