Read a Confusion Matrix Like a Decision Tool
Interpret a confusion matrix and connect false positives and false negatives to business consequences.
A fraud model reviews 20,000 transactions. Actual fraud cases = 250. The model flags 1,080 transactions. Of those flags, 180 are real fraud and 900 are good transactions. Confusion matrix = compare actual class against predicted class, then interpret each error as a workflow consequence. The common trap is celebrating high accuracy while ignoring which mistakes users experience. True positives 180 transactions were fraud and were flagged These are successful catches. False positives 900 transactions were good but were flagged These create customer friction and support load. False negatives 250 actual fraud - 180 caught = 70 missed fraud cases…
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