Tell me about ROC-AUC: what is it, and how is it used?

ROC-AUC is a metric for the quality of binary classifiers, measuring the model's ability to distinguish between classes.

• ROC (Receiver Operating Characteristic) — a curve showing the dependence of True Positive Rate (sensitivity) on False Positive Rate at various classification thresholds.
• AUC (Area Under Curve) — the area under the ROC curve, numerically expressing the model's quality.

The AUC value ranges from 0 to 1:

• 0.5 — the model is no better than random guessing.
• Closer to 1 — the model distinguishes classes well.

It is used to evaluate models, especially when classes are imbalanced, as it considers all possible thresholds and does not depend on a specific classification threshold.

Example of usage in Python with scikit-learn library:

from sklearn.metrics import roc_auc_score

y_true = [0, 0, 1, 1]
y_scores = [0.1, 0.4, 0.35, 0.8]

auc = roc_auc_score(y_true, y_scores)
print(f"ROC-AUC: {auc}")

Thus, ROC-AUC helps understand how well the model can separate positive examples from negative ones.