Result
Enter TP, FP, FN, and TN to calculate classification metrics.
Confusion Matrix Calculator
Calculate accuracy, precision, recall, F1 score, and related classification metrics from TP, FP, TN, and FN.
Inputs
Pred +
Pred -
Actual +
Actual -
Metrics
Accuracy-
Precision-
Recall-
Specificity-
F1-
FPR-
FNR-
NPV-
Total-
Formula
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Specificity = TN / (TN + FP)
F1 = 2 × Precision × Recall / (Precision + Recall)
Steps
Enter TP, FP, FN, and TN, then solve to see the calculation steps.
Model Evaluation Made Clear
Why Use a Confusion Matrix Calculator?
A confusion matrix calculator helps you turn prediction results into meaningful performance metrics, so you can understand how well a classification model is really working.
Clear Accuracy Insights
See how many predictions were correct overall, while still keeping the details visible for true positives, false positives, true negatives, and false negatives.
Error Pattern Visibility
Accuracy alone can hide important mistakes. A confusion matrix shows exactly where your model is confusing one class for another.
Precision and Recall Review
Compare how reliable positive predictions are and how well the model captures actual positives, especially when the cost of missed cases matters.
Better Imbalanced Data Checks
When one class appears much more often than another, the matrix helps reveal whether the model is genuinely useful or mostly guessing the majority class.
F1 Score Understanding
Use the calculated F1 score to judge performance when precision and recall both matter, such as fraud detection, medical screening, or risk alerts.
Report-Ready Results
Quickly translate raw prediction counts into metrics that are easier to explain in experiments, dashboards, presentations, and model documentation.
Simple Workflow
How to Use the Confusion Matrix Calculator
Enter the four core prediction counts, then review the calculated metrics to understand your model from several practical angles.
01
Add the Prediction Counts
Start with true positives, false positives, true negatives, and false negatives. These values describe what your model predicted compared with what actually happened.
02
Review the Main Metrics
Check accuracy, precision, recall, specificity, and F1 score. Each metric answers a slightly different question about model quality and reliability.
03
Compare Results in Context
Use the numbers alongside your real goal. A model for spam filtering, loan risk, medical screening, or quality control may need a different balance of errors.
Practical Applications
Where Confusion Matrix Results Are Useful
Confusion matrix metrics are useful anywhere you need to explain, compare, or improve a classification model with confidence.
Machine Learning Reports
Summarize classification performance in a format that data scientists, analysts, and stakeholders can understand without digging through raw predictions.
Model Comparison
Compare multiple models using consistent metrics, making it easier to choose the best version before deployment or further tuning.
Quality Control Systems
Evaluate defect detection, pass-fail classification, and inspection models where false alarms and missed problems both affect operations.
Fraud and Risk Detection
Understand how often suspicious cases are caught and how many normal cases are incorrectly flagged by the model.
Screening and Diagnostics
Analyze sensitivity, specificity, and false negatives in high-impact classification tasks where missing positive cases can be costly.
Dashboard Explanations
Add understandable performance context to analytics dashboards, internal reviews, research notes, and decision-support tools.
Helpful by Design
Built for Fast, Reliable Evaluation
A good confusion matrix calculator should feel simple to use, easy to read, and useful whether you are learning the basics or reviewing a production model.
No Signup Required
Use the calculator directly whenever you need a quick model performance check, without account creation or unnecessary steps.
Fast Metric Review
Move from raw counts to practical metrics in seconds, so you can spend more time interpreting results and less time repeating formulas.
Works on Any Screen
The content and results are easy to scan on desktop, tablet, and mobile, making it useful during study, reporting, or live model review.
