ROC-AUC and Precision-Recall AUC are both used to evaluate classification models — but they answer very different questions. In this video, we break down what each metric actually measures, where ROC-AUC can be misleading (especially with imbalanced data), and how to pick the right one based on your deployment scenario.
🕛Timestamps:
0:00 — Introduction
1:06 — What decision is your model supporting?
1:57 — How classification scores and thresholds work
3:14 — What ROC-AUC measures (and why it's useful)
4:34 — The catch: Why ROC-AUC can be misleading under class imbalance
5:09 — What Precision-Recall AUC focuses on
5:54 — Fraud model example: When good ROC-AUC hides bad performance
6:49 — Why AUC alone isn't enough — threshold-level evaluation
7:41 — When to use ROC-AUC vs PR-AUC
8:24 — The decision rule to remember
9:26 — Wrap-up
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