Confusion Matrix Calculator

Calculate, analyze, and visualize classification model evaluation metrics including Accuracy, Precision, Recall, F1-Score, MCC, and Cohen's Kappa instantly in your browser.

Enter the raw counts for your binary classification matrix.

Paste your actual classes (0 or 1) and predicted probabilities (0.0 to 1.0) separated by comma or tab. Adjust the threshold to see how metrics change.

Data (Actual, Probability) - one pair per line:

Classification Threshold: 0.50

Paste Actual vs Predicted labels (comma or tab separated) OR upload a CSV file.

Raw Data (Actual, Predicted):

Or Upload CSV File:

Analysis Results

Confusion Matrix

View:

* Color intensity indicates proportion. Diagonal (green/blue) = Correct, Off-diagonal (red) = Incorrect.

Core Evaluation Metrics

Per-Class Metrics & Averages

Class	PrecisionTP / (TP + FP) for this class	RecallTP / (TP + FN) for this class	F1-ScoreHarmonic mean of Precision & Recall	SpecificityTN / (TN + FP) One-vs-Rest	Support (Actual)

Performance Visualization

Result Interpretation

Formulas & Guide

Metric	Formula	Description
Accuracy	`(TP + TN) / (TP + TN + FP + FN)`	Overall proportion of correct predictions.
Error Rate	`(FP + FN) / Total`	Proportion of incorrect predictions.
Precision	`TP / (TP + FP)`	Out of all predicted positive, how many were actually positive?
Recall / Sensitivity	`TP / (TP + FN)`	Out of all actual positives, how many were correctly predicted?
Specificity	`TN / (TN + FP)`	Out of all actual negatives, how many were correctly predicted?
F1-Score	`2 * (Precision * Recall) / (Precision + Recall)`	Harmonic mean of precision and recall. Best for imbalanced datasets.
MCC	`(TPTN - FPFN) / sqrt(...)`	Matthews Correlation Coefficient. Range [-1, 1]. Reliable even on imbalanced data.

Frequently Asked Questions

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