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AI & Machine Learning · Foundational

Confusion Matrix

A table comparing predicted classes with actual classes, showing true/false positives and negatives.

Definition

For binary classification the matrix has four cells: true positives, false positives (Type I errors), false negatives (Type II errors) and true negatives. Precision, recall, specificity, accuracy and F1 are all computed from these cells.

Example

TP=80, FP=20, FN=10, TN=890 gives precision 0.80 and recall 0.89.

Exam tip

Be ready to compute precision and recall from a confusion matrix by hand.

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