What is AUC-ROC curve?

Question

Accepted Answer

AUC-ROC curve is a graphical representation of the performance of a classification model.

AUC-ROC stands for Area Under the Receiver Operating Characteristic curve.
It is used to evaluate the performance of binary classification models.
The curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various classification thresholds.
AUC-ROC ranges from 0 to 1, with a higher value indicating better model performance.
An AUC-ROC of 0.5 represents a random classifier, while an AUC-ROC of 1 represents a perfect classifier.
AUC-ROC can be used to compare different models and select the one with the highest AUC-ROC value.

Accepted Answer

AUC-ROC are the methods to evaluate the performance of classification problems.

ROC stands for receiver operator characteristics. This is a plot between the true positive rate (recall) and the false positive rate (1 - specificity). We can create the plot for this using different values of the cutoff parameter for the classification model.

This plot helps us identify the appropriate cutoff value for our use case by defining how much of a false positive is acceptable.

The area under the ROC curve is known as AUC (area under curve). Higher the area under the curve better the performance of the model. We can compare different classification models based on AUC.

What is AUC-ROC curve?

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