ROC AUC vs PR AUC | Explained Visually

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 Subscribe to Schovia for more ML concepts, clearly explained. Understand AI Beyond the Buzzwords — ML, Deep Learning, LLMs, and AI Systems. #MachineLearning #ROCAUC #PrecisionRecall #ModelEvaluation #DataScience #MLMetrics #ClassImbalance #SchoviaLabs#DataScience #ArtificialIntelligence #MLAlgorithms #AIForBeginners #AIModels #PredictiveAnalytics #AIClubPro #MLTutorial #AITraining #TechEducation #MLFundamentals