What is the significance of the AUC in ROC analysis?

It measures the model's training time
It indicates the model's accuracy
It represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance
It shows the number of features used in the model

What is the significance of the AUC in ROC analysis?

Step 1: Understand that ROC stands for Receiver Operating Characteristic, which is a graph that shows the performance of a classification model.
Step 2: Know that the curve on the ROC graph plots the true positive rate against the false positive rate at various threshold settings.
Step 3: Learn that AUC stands for Area Under the Curve, which measures the entire two-dimensional area underneath the ROC curve.
Step 4: Realize that the AUC value ranges from 0 to 1, where 1 means perfect discrimination between positive and negative classes, and 0.5 means no discrimination (like random guessing).
Step 5: Understand that a higher AUC value indicates a better model performance in distinguishing between the positive and negative classes.

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