juniorModel Evaluation
What is a confusion matrix in model evaluation?
Updated May 17, 2026
Short answer
A confusion matrix is a table used to evaluate classification models by comparing predicted vs actual labels.
Deep explanation
A confusion matrix summarizes performance by counting true positives, true negatives, false positives, and false negatives. It helps understand not just accuracy but the types of errors a model makes.
Real-world example
Used in fraud detection to see how many fraudulent transactions were correctly or incorrectly flagged.
Common mistakes
- Assuming accuracy alone is sufficient without analyzing false positives and negatives.
Follow-up questions
- What do FP and FN represent?
- How does imbalance affect it?