Artificial Intelligence & Machine Learning
AUC
Definition
AUC stands for Area Under the ROC Curve. It is a performance measurement for classification problems. AUC represents the degree or measure of separability between classes. It tells how much the model is capable of distinguishing between classes.
Why It Matters
AUC provides a single, aggregate measure of a model's performance across all possible classification thresholds. This makes it a useful and robust metric for comparing different models, especially on imbalanced datasets.
Contextual Example
If Model A has an AUC of 0.85 and Model B has an AUC of 0.92, it generally means that Model B is better at distinguishing between the positive and negative classes.
Common Misunderstandings
- An AUC of 1.0 means the model is a perfect classifier.
- An AUC of 0.5 means the model has no discriminative ability; it's as good as random guessing.