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Can I use ROC curve for regression?
ROC curves neatly visualise how the TPR and the FPR change for different (crisp) classifiers or evolve for the same (soft) classifier (or ranker) for a range of thresholds. Some of these approaches actually convert the evaluation of a regression problem into a classification problem (tolerable estimation vs.
Can AUC ROC be used for both classification as well as regression problems?
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.
Is ROC curve only logistic regression?
The ROC curve is not only useful for logistic regression results. In fact we can use the ROC curve and the AUC to assess the performance of any binary classifier.
Which is the best ROC curve for regression?
Notable efforts towards ROC curves (or graphical tools) for regression are the Regression Error Characteristic (REC) Curves [3], the Regression Error Characteristic Surfaces (RECS) [51], the notion of utility-based regression [41] and the definition of ranking measures [43].
What does area under ROC curve tell us?
The Area Under the ROC curve (AUROC) tells for a given range of the controlling parameter “what is the probability of the classifier to rank a randomly chosen positive instance higher than a randomly chosen negative instance, (assuming ‘positive’ ranks higher than ‘negative’)”, [3,4]
How is the ROC curve used in binary classification?
You can’t, really. A (binary) classification task has a small set of possible outcomes: you either correctly detect/reject something or you don’t. The ROC curve measures the trade-off between these (specifically, between the false positive rate and the true positive rate).
How is the ROC space for regression defined?
The ROC space for regression (RROC space) is then defined by placing the total over-estimation on the x -axis and the total under-estimation on the y -axis. This duality leads to regions and isometrics in the ROC space where over-estimations have less cost than under-estimations and vice versa.