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Does AUC change with threshold?
Note: AUC is not dependent on classification threshold value. Changing the threshold value does not change AUC because it is an aggregate measure of ROC.
What is the threshold in AUC ROC?
In a ROC curve, a higher X-axis value indicates a higher number of False positives than True negatives. While a higher Y-axis value indicates a higher number of True positives than False negatives. So, the choice of the threshold depends on the ability to balance between False positives and False negatives.
Is AUC based on a single threshold?
As a general performance metric, AUC measures the binary classification model performance without the need to specify a threshold. Note that AUC could only be calculated if the model is capable of providing probability prediction.
How do you get AUC from ROC curve?
The AUC for the ROC can be calculated using the roc_auc_score() function. Like the roc_curve() function, the AUC function takes both the true outcomes (0,1) from the test set and the predicted probabilities for the 1 class. It returns the AUC score between 0.0 and 1.0 for no skill and perfect skill respectively.
How to define the thresholds for the ROC curve?
When plotting the ROC (or deriving the AUC) in scikit-learn, how can one specify arbitrary thresholds for roc_curve, rather than having the function calculate them internally and return them?
When to compare ROC curves and AUC values?
To your question: first, if you want to compare different approaches, comparing their ROC curves and area under curve (AUC) values directly will be a good idea, as those give you overall information about how powerful your approaches are on your problem. Second: you will need to choose a threshold appropriate for your goal.
Is the AUC value of a model significant?
By itself, it does not have any significance. Whereas — say you have the prediction results from 2 models — one with the value of 0.96 and other with 0.88, then you could determine the model having higher AUC is better for your data. In Machine Learning, performance measurement is an essential task.
How is the ROC curve used in logistic regression?
The ROC curve is produced by calculating and plotting the true positive rate against the false positive rate for a single classifier at a variety of thresholds. For example, in logistic regression, the threshold would be the predicted probability of an observation belonging to the positive class.