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Confusion Matrix Metrics Calculator — result sheet
Calculate accuracy, precision, recall, specificity, and F1 score from a binary confusion matrix.
Inputs used
Results
Visual chart
Breakdown
Calculation steps
Returned data table
Formula and methodology
Formula: Accuracy=(TP+TN)/N; precision=TP/(TP+FP); recall=TP/(TP+FN); specificity=TN/(TN+FP); F1=2PR/(P+R).
Each metric is returned separately so class-specific trade-offs are not hidden behind accuracy.
This result follows the calculator's declared inputs, precision, validation boundaries, and model limits.
Input contract
- True positives — minimum 0; maximum 1000000000
- True negatives — minimum 0; maximum 1000000000
- False positives — minimum 0; maximum 1000000000
- False negatives — minimum 0; maximum 1000000000
Worked example
| Input | Value |
|---|---|
| True positives | 80 |
| True negatives | 90 |
| False positives | 10 |
| False negatives | 20 |
Accuracy =85%; precision ≈88.89%; recall =80%; specificity =90%; F1≈84.21%.
Assumptions and limits
- Counts are nonnegative whole numbers and refer to one binary evaluation set.
- Undefined zero-denominator metrics are reported as zero by convention.
- Threshold selection, calibration, prevalence shift, costs, and deployment performance are not modeled.
Calculator note
Source and methodology
Use the official WorldCalculate methodology policy for the source, formula, precision, and boundary standards behind this calculator.
Planning estimate, not financial, medical, legal, or professional advice. © WorldCalculate — reuse with attribution. Built and curated by Hassan ALRowaie.
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