Accuracy is the proportion of all predictions that are correct: (TP + TN) / (TP + FP + FN + TN). It is intuitive but can be misleading when classes are imbalanced.

What is the difference between precision and recall?

Precision measures how many of the predicted positives are actually positive: TP / (TP + FP). Recall (sensitivity) measures how many actual positives were correctly identified: TP / (TP + FN). High precision means fewer false alarms; high recall means fewer missed positives.

What is the F1 score?

F1 is the harmonic mean of precision and recall: 2 * (Precision * Recall) / (Precision + Recall). It balances both metrics and is especially useful when classes are imbalanced.

Specificity (true negative rate) is TN / (TN + FP). It measures how well the model identifies actual negatives — important in medical screening where false positives have a cost.

Why does the tool show N/A for some metrics?

Some metrics are undefined when their denominator is zero. For example, precision is N/A if TP + FP = 0 (the model made no positive predictions). This is mathematically correct, not a bug.

Does this support multi-class classification?

No. This tool is designed for binary (two-class) classification using a standard 2x2 confusion matrix. For multi-class models, compute one-vs-rest confusion matrices and average the metrics.

Accuracy Calculator

Name: Accuracy Calculator
Availability: InStock
Author: Nham Vu

Enter TP, FP, FN, TN from a confusion matrix to instantly calculate accuracy, precision, recall, F1 score, and specificity.

Confusion Matrix

Enter counts from your 2×2 confusion matrix.

TP — True Positives Correctly predicted positive

FP — False Positives Negative predicted as positive

FN — False Negatives Positive predicted as negative

TN — True Negatives Correctly predicted negative

Enter your confusion matrix values
and click Calculate to see metrics.

Copied!

Summary

Enter TP, FP, FN, TN from a confusion matrix to instantly calculate accuracy, precision, recall, F1 score, and specificity.

How it works

Enter the number of True Positives (TP) — correct positive predictions.
Enter False Positives (FP) — negatives incorrectly predicted as positive.
Enter False Negatives (FN) — positives incorrectly predicted as negative.
Enter True Negatives (TN) — correct negative predictions.
Click "Calculate" (or edit any field) to see all five metrics update instantly.
Use the Reset button to clear all values and start over.

Use cases

Evaluate a machine learning classifier after training.
Compare model performance across different thresholds.
Report precision, recall, and F1 in an academic paper or presentation.
Diagnose whether a model is biased toward false positives or false negatives.
Compute specificity for medical test evaluation.
Quickly verify hand-calculated confusion matrix metrics.
Teach classification evaluation concepts in a data science course.
Audit model quality during code review or model review sessions.

Frequently Asked Questions

Last updated: 2026-06-09 · Reviewed by Nham Vu