Your model has 95% accuracy. You ship it. Three weeks later someone tells you it's missing 40% of actual fraud cases. You check. The dataset had 95% legit transactions and 5% fraud. Your model just learned to say "not fraud" every single time. 95% accuracy. Zero fraud caught. That's what happens when you trust accuracy alone. The confusion matrix is the tool that would have caught this immediately. What You'll Learn Here What the four cells of a confusion matrix mean TP, TN, FP, FN with real-world examples not textbook ones How to build and read a confusion matrix in Python Why class imbalance makes accuracy useless How to visualize it properly Multi-class confusion matrices The Four Outcomes of Every Prediction Every prediction your model makes falls into one of four buckets. Let's use a disease test as the example because the stakes are obvious. PREDICTED Positive Negative ACTUAL Positive | TP | FN | Negative | FP | TN | Enter fullscreen mode Exit fullscreen mode True Positive (TP): Model said positive.…