What is a confusion matrix?

A confusion matrix is a 2x2 table that shows how a binary classifier performs: TP (correctly predicted positives), FP (negatives predicted as positive), FN (positives predicted as negative), and TN (correctly predicted negatives).

What is the difference between precision and recall?

Precision (TP / (TP + FP)) measures how many predicted positives are actually positive — important when false alarms are costly. Recall (TP / (TP + FN)) measures how many actual positives were found — important when missing a positive is costly.

When should I use F1 score vs. accuracy?

Accuracy is misleading on imbalanced datasets. If 95% of samples are negative, a model that always predicts negative gets 95% accuracy but detects nothing. F1 is the harmonic mean of precision and recall and is more informative when classes are unequal.

What is MCC (Matthews Correlation Coefficient)?

MCC ranges from -1 (perfect inverse prediction) through 0 (random) to +1 (perfect prediction). It accounts for all four quadrants of the confusion matrix and is considered one of the most balanced metrics for binary classification, especially on imbalanced datasets.

What does specificity measure?

Specificity (TN / (TN + FP)) is the true negative rate — the proportion of actual negatives correctly identified. It is the complement of the false positive rate and is widely used in medical diagnostics.

Can this calculator handle multi-class problems?

No — this tool is designed for binary (2-class) classification only. For multi-class problems, metrics are typically computed per class using a one-vs-rest approach and then averaged (macro, micro, or weighted).

Confusion Matrix Calculator

Name: Confusion Matrix Calculator
Availability: InStock
Author: Nham Vu

Enter TP, FP, FN, and TN values to instantly compute accuracy, precision, recall, F1 score, specificity, and Matthews Correlation Coefficient.

Confusion Matrix Inputs

Enter the four counts from your classifier's confusion matrix.

TP — True Positive

Actual +, predicted +

FP — False Positive

Actual −, predicted +

FN — False Negative

Actual +, predicted −

TN — True Negative

Actual −, predicted −

Matrix Visualization

	Predicted
	Positive	Negative
Actual +	—	—
Actual −	—	—

Correct Incorrect

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

Total Samples —

TP FP FN TN

Accuracy

—

(TP+TN)/N

Overall fraction of correct predictions.

Precision

—

TP/(TP+FP)

Of all predicted positives, how many are correct.

Recall (Sensitivity)

—

TP/(TP+FN)

Of all actual positives, how many were found.

F1 Score

—

2·P·R/(P+R)

Harmonic mean of precision and recall.

Specificity

—

TN/(TN+FP)

Of all actual negatives, how many were correctly identified.

MCC

—

[-1, +1]

Matthews Correlation Coefficient. +1 = perfect, 0 = random, -1 = inverse.

Additional Rates

False Positive Rate

—

False Negative Rate

—

False Discovery Rate

—

Balanced Accuracy

—

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Summary

Enter TP, FP, FN, and TN values to instantly compute accuracy, precision, recall, F1 score, specificity, and Matthews Correlation Coefficient.

How it works

Enter the four confusion matrix counts: TP, FP, FN, and TN.
The calculator instantly derives accuracy, precision, recall, F1 score, specificity, and MCC.
A color-coded 2x2 matrix visualization updates in real time.
Metric cards show the value and a plain-English interpretation.
Use the Reset button to clear all fields and start over.

Use cases

Evaluate a binary classifier such as a spam filter or fraud detector.
Compare multiple model checkpoints by their F1 or MCC scores.
Explain classification performance to stakeholders without writing code.
Check whether precision or recall should be prioritized for your use case.
Verify hand-calculated metrics from a research paper or report.
Understand the trade-off between sensitivity and specificity in medical tests.

Frequently Asked Questions

Last updated: 2026-06-10 · Reviewed by Nham Vu