Confusion Matrix & ROC Curve - Interactive Visualization

A confusion matrix breaks every prediction into four buckets: true positives, false positives, true negatives, and false negatives. By dragging the decision threshold, you can see exactly how precision and recall trade off - and how that tradeoff traces out the ROC curve. The AUC (area under the ROC curve) gives a threshold-independent measure of classifier quality.

• Understand TP, FP, TN, FN with live counts as you move the classification threshold
• See precision and recall move in opposite directions as threshold shifts - the core tradeoff every ML engineer faces
• Watch the operating point trace the ROC curve and understand what AUC = 0.5 vs 1.0 means
• Learn when to optimize for precision (spam filter) vs recall (cancer screening)
• F1 score combines precision and recall into one metric - see exactly when it peaks

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.