Confusion Matrix Precision, Recall, F1-Score, Specificity & MCC Calculator

Evaluate binary classifiers using F1-Score and Matthews Correlation Coefficient (MCC).

F1-Score (Harmonic Mean)
91.3 %
Precision (Positive Predictive Value) 90.32 %
Recall / Sensitivity (TPR) 92.31 %
Matthews Correlation Coefficient (MCC) 0.89 MCC
Inputs
TP
True Positives (TP) count.
FP
False Positives (FP / Type I Error) count.
FN
False Negatives (FN / Type II Error) count.
TN
True Negatives (TN) count.
Results & Graph
F1-Score (Harmonic Mean)
91.3 %
Precision (Positive Predictive Value)
90.32 %
Recall / Sensitivity (TPR)
92.31 %
Matthews Correlation Coefficient (MCC)
0.89 MCC

F1-Score (Harmonic Mean)

Graph 1 • True Positives Tp Sensitivity Recalculates the domain equation across 50% to 150% of the primary input while holding other parameters constant.
0 % 50.8 % 101.6 % 50%: 84 % 84 50% 75%: 88.73 % 88.7 75% 100%: 91.3 % 91.3 100% 125%: 92.92 % 92.9 125% 150%: 94.03 % 94.0 150%
Graph 2 • Primary & Supporting Metric Comparison Compares the primary output alongside supporting domain benchmarks.
F1-Score (Harmonic Mean) 91.3 % Precision (Positive Predictive Value) 90.32 % Recall / Sensitivity (TPR) 92.31 % Matthews Correlation Coefficient (MCC) 0.89 MCC
Graph 3 • Exact Output Table & Units Displays each calculated output in its exact domain unit.
F1-Score (Harmonic Mean)91.3 %Precision (Positive Predictive Value)90.32 %Recall / Sensitivity (TPR)92.31 %Matthews Correlation Coefficient (MCC)0.89 MCC
Summary: Precision is 90.32%, Recall is 92.31%, F1-Score is 91.3%, and Matthews Correlation Coefficient (MCC) is 0.8872.
Summary: Precision is 90.32%, Recall is 92.31%, F1-Score is 91.3%, and Matthews Correlation Coefficient (MCC) is 0.8872.
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Formula & Mathematical Methodology: Confusion Matrix & MCC Systems Engineering Formula

Precision = TP/(TP+FP)
Recall = TP/(TP+FN)
F1 = 2·P·R/(P+R)
MCC = (TP·TN − FP·FN) / (TP+FP(TP+FN)(TN+FP)(TN+FN))
Precision = TP/(TP+FP); Recall = TP/(TP+FN); F1 = 2·P·R/(P+R); MCC = (TP·TN − FP·FN) / √((TP+FP)(TP+FN)(TN+FP)(TN+FN))
  • truePositivesTp: True Positives Tp (TP)
  • falsePositivesFp: False Positives Fp (FP)
  • falseNegativesFn: False Negatives Fn (FN)
  • trueNegativesTn: True Negatives Tn (TN)

Rounding: Binary/decimal capacities and engineering metrics retain full floating-point precision and round to 2 decimal places.

Worked Example: Architecture Sizing Example: Confusion Matrix & MCC

Evaluate binary classifiers using F1-Score and Matthews Correlation Coefficient (MCC).

  1. Enter the workload parameters, model dimensions, or network link specifications.
  2. Compute the sizing equation: Precision = TP/(TP+FP); Recall = TP/(TP+FN); F1 = 2·P·R/(P+R); MCC = (TP·TN − FP·FN) / √((TP+FP)(TP+FN)(TN+FP)(TN+FN)).
  3. Inspect the primary capacity metric, headroom buffers, and 3 SVG visualizations.
Result: Calculates Confusion Matrix & MCC via Precision = TP/(TP+FP); Recall = TP/(TP+FN); F1 = 2·P·R/(P+R); MCC = (TP·TN − FP·FN) / √((TP+FP)(TP+FN)(TN+FP)(TN+FN)).

Frequently Asked Questions

How does the Confusion Matrix Precision, Recall, F1-Score, Specificity & MCC Calculator compute capacity?

It evaluates Precision = TP/(TP+FP); Recall = TP/(TP+FN); F1 = 2·P·R/(P+R); MCC = (TP·TN − FP·FN) / √((TP+FP)(TP+FN)(TN+FP)(TN+FN)) directly in your browser and plots three custom SVG diagrams.

Can I compare different cluster or model configurations side by side?

Yes. Use the "Compare (0/3)" button in the results panel to pin up to three configurations.

Does this tool upload any of my architecture parameters?

No. All calculations run 100% client-side in your browser.

Mathematics & Statistics · Machine Learning & Evaluation

Confusion Matrix Precision, Recall, F1-Score, Specificity & MCC Calculator: inputs, method, and how to read the result

Evaluate binary classifiers using F1-Score and Matthews Correlation Coefficient (MCC).

Choose inputs for the case you are checking

The defaults are illustrative. Replace them with values for your case and keep the units shown beside each field.

  • True Positives Tp: True Positives (TP) count.
  • False Positives Fp: False Positives (FP / Type I Error) count.
  • False Negatives Fn: False Negatives (FN / Type II Error) count.
  • True Negatives Tn: True Negatives (TN) count.

What the calculation includes

With the default values shown, F1-Score (Harmonic Mean) is 91.3 %. The calculation follows Confusion Matrix & MCC Systems Engineering Formula; the formula and variable definitions above show how the inputs combine.

One-input check: With the other defaults held constant, changing True Positives Tp from 420 TP to 462 TP changes F1-Score (Harmonic Mean) from 91.3 % to 92.03 %. This is a comparison inside the calculator's stated model, not a prediction.

Where to use caution

  • Uses industry-standard IEEE, IETF RFC, and GPU/cloud hardware sizing conventions.
  • Capacity planning estimate; benchmark under production traffic patterns before finalizing cluster or hardware procurement.

Scope: IEEE / IETF / Cloud Systems Engineering Standard Check the assumptions and limitations against your situation before relying on the estimate.

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