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ML Classification Confusion Matrix & F1 Calculator

Confusion Matrix 2×2 Inputs

True Positive (TP)
False Positive (FP)
False Negative (FN)
True Negative (TN)
Accuracy
93.33%
Precision
87.63%
Recall / Sens.
91.40%
F1-Score
89.47%
Specificity
94.20%

ML Classification Confusion Matrix & F1 Calculator

4.9•320K Users•100% Client-Side Privacy
Unlimited Free

Calculate classification metrics including Accuracy, Precision, Recall, F1-Score, and Specificity from 2x2 confusion matrix values.

Precision & RecallF1-Score calculationSpecificity metricInteractive 2x2 grid

How to Use ML Classification Confusion Matrix & F1 Calculator

Follow these simple steps to process your files securely in browser memory.

1Step 1 of 3

Enter Matrix Cell Counts

Input True Positives, False Positives, False Negatives, and True Negatives.

Pro Tip: In medical screening, maximizing Recall is vital to minimize missed cases.
2Step 2 of 3

Inspect Real-Time Metrics

Calculates Accuracy, Precision, Recall, Specificity, and F1-Score instantly.

Pro Tip: F1-Score balances Precision and Recall for class-imbalanced data.
3Step 3 of 3

Export LaTeX & JSON Metrics

Copy formatted LaTeX tables and JSON metric summaries for ML reports.

Pro Tip: Matthew's Correlation Coefficient (MCC) provides robust evaluation for skewed classes.

Who Is ML Classification Confusion Matrix & F1 Calculator Built For?

Designed for professionals seeking fast, private, and unlimited client-side execution.

Primary Target Audience
🎯Machine Learning Engineers & Data Scientists
Also Widely Used By
Medical StatisticiansML Students
Typical Real-World Use Cases
  • Evaluating binary classification models (TP, TN, FP, FN)
  • Computing Accuracy, Precision, Recall, Specificity, F1-Score
  • Diagnosing imbalanced dataset bias

Complete Classification Suite

Accuracy, Precision, Recall, Specificity, F1, and MCC.

Real-Time 2x2 Visualizer

Live metric recalculation as values change.

LaTeX & Markdown Export

Instant copy for academic papers and reports.

Frequently Asked Questions about ML Classification Confusion Matrix & F1 Calculator

F1 = 2 * (Precision * Recall) / (Precision + Recall).
Top Search Queries for ML Classification Confusion Matrix & F1 Calculator:
confusion matrix calculator online f1 scoreprecision recall specificity accuracy calculatormachine learning 2x2 confusion matrix solvercalculate f1 score from tp fp fn tnclassification metrics generator data science

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