63. Confusion Matrix: What Your Model Got Wrong and Why

Short summary

Confusion matrices break model predictions into four categories—true positives, true negatives, false positives, and false negatives—revealing what models actually learn. A 95% accuracy model catching zero fraud illustrates why accuracy alone misleads, especially with imbalanced classes. Use confusion matrices to evaluate the true cost of each error type.

• •Confusion matrices expose prediction patterns by categorizing into TP, TN, FP, FN
• •Accuracy alone is misleading with imbalanced datasets—Model A with 95% accuracy caught zero fraud
• •Use confusion matrices to balance the real-world cost of false positives vs false negatives

Generated with AI, which can make mistakes.