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Which is better ROC or AUC for imbalanced data?
If two ROCs cross, the ROC with the higher AUC will have at least a measurable subset of thresholds where ROC with inferior AUC is a better classifier. What about imbalanced data? So if I have 95 data points of class 1 and 5 of class 2 and my classifier always predicts class 1, i would still have a accuracy of 95%.
How to calculate the AUC for imbalanced learning?
The most common metric involves receiver operation characteristics (ROC) analysis, and the area under the ROC curve (AUC). — Page 27, Imbalanced Learning: Foundations, Algorithms, and Applications, 2013. The AUC for the ROC can be calculated in scikit-learn using the roc_auc_score () function.
Why is the ROC curve misleading for imbalanced classification?
For imbalanced classification with a severe skew and few examples of the minority class, the ROC AUC can be misleading. This is because a small number of correct or incorrect predictions can result in a large change in the ROC Curve or ROC AUC score.
Which is better precision recall or ROC curve?
I’ve been reading the paper ” The Relationship Between Precision-Recall and ROC Curves ” recently, which argues that at problems suffering from class imbalance problem, using an evaluation metric of Precision-Recall AUC (PR AUC) is better than Receiver-Operating-Characteristic AUC (ROC AUC).
Why is the ROC curve so imbalanced?
While the blue, w=1, line performed poorly in both charts, the black, w=10000, line performed “well” in the ROC but poorly in the PR curve. This is due to the high class imbalance in our data.
When is AUC misleading due to imbalanced data?
By random chance, your AUC can be misleadingly high, or misleadingly low with equal probability if you predict all ones with a small sample of ones. One way in which AUC can be misleadingly high is if you have imbalanced data in favor of 0 s, and a model with high recall but low precision, as I explained here.
Is the PR curve the same as the ROC curve?
While PR and ROC curves use the same data, i.e. the real class labels and predicted probability for the class lables, you can see that the two charts tell very different stories, with some weights seem to perform better in ROC than in the PR curve.