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Open toolConfusion Matrix Calculator
Build a binary confusion matrix from counts or paired labels and calculate its basic metrics. Provide Input mode, True positives, False positives to compute results directly in your browser. Labels are 0 or 1, with 1 as the positive class. Accuracy, precision, recall and F1 use standard binary counts. A zero metric denominator is reported as undefined; total observation count must be positive. Numeric output uses up to 12 significant digits; floating-point rounding can occur. Common search workflows supported include "space ether strain 6 1 5 true false false true true true 40 3 15 11 0 99 1 0 0 ai overview", "confusion matrix calculator".
Free to useNo sign-upRuns in your browser
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Enter the inputs, then select Calculate.
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How the Confusion Matrix Calculator Works & Core Formula
The Confusion Matrix Calculator evaluates Input mode, True positives, False positives in client-side JavaScript using the deterministic rule below and updates the output panel immediately without uploading data to a server.
Build a binary confusion matrix from counts or paired labels and calculate its basic metrics.
Labels are 0 or 1, with 1 as the positive class. Accuracy, precision, recall and F1 use standard binary counts. A zero metric denominator is reported as undefined; total observation count must be positive. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
Confusion Matrix Calculator Quick Reference & Input Guide
Review the input parameters, allowed value ranges, and worked reference cases for the Confusion Matrix Calculator below before running your calculation.
| Parameter / Scenario | Default / Sample Value | Rule or Allowed Range | Output / Usage Note |
|---|---|---|---|
| Input: Input mode | Counts | Options: Counts, Binary labels | Required workspace parameter |
| Input: True positives | 40 | Range: 0 to 1000000000 | Required workspace parameter |
| Input: False positives | 10 | Range: 0 to 1000000000 | Required workspace parameter |
| Input: False negatives | 5 | Range: 0 to 1000000000 | Required workspace parameter |
| Worked Case 1: Baseline run (Input mode = Counts) | Preloaded / test values | Build a binary confusion matrix from counts or paired labels and calculate its basic metrics | Input mode = Counts; True positives = 40; False positives = 10; False negatives = 5; True negatives = 45; Actual binary labels = 1,1,0,0; Predicted binary labels = 1,0,1,0. Output excerpt: TP: 40; FP: 10; FN: 5; TN: 45. |
| Worked Case 2: Comparison scenario (confusion matrix calculator online) | Preloaded / test values | Build a binary confusion matrix from counts or paired labels and calculate its basic metrics | Input mode = Binary labels; True positives = 40; False positives = 10; False negatives = 5; True negatives = 45; Actual binary labels = 1,1,0,0; Predicted binary labels = 1,0,1,0. Output excerpt: TP: 1; FP: 1; FN: 1; TN: 1. |
Step-by-Step Guide to Using the Confusion Matrix Calculator
Follow these three steps to configure Input mode, True positives, False positives, verify unit and boundary rules, and export your Confusion Matrix Calculator output.
Input and settings
Check units and mode
Labels are 0 or 1, with 1 as the positive class. Accuracy, precision, recall and F1 use standard binary counts. A zero metric denominator is reported as undefined; total observation count must be positive. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
Tool result
Calculate and save
Select Calculate, check the labelled result, then use Copy result or Download result.txt.
Review the details
Practical Worked Examples for Confusion Matrix Calculator
These concrete scenarios illustrate how the Confusion Matrix Calculator processes baseline inputs, comparison checks, and boundary constraints.
Baseline run (Input mode = Counts)
Input mode = Counts; True positives = 40; False positives = 10; False negatives = 5; True negatives = 45; Actual binary labels = 1,1,0,0; Predicted binary labels = 1,0,1,0. Output excerpt: TP: 40; FP: 10; FN: 5; TN: 45.
Comparison scenario (confusion matrix calculator online)
Input mode = Binary labels; True positives = 40; False positives = 10; False negatives = 5; True negatives = 45; Actual binary labels = 1,1,0,0; Predicted binary labels = 1,0,1,0. Output excerpt: TP: 1; FP: 1; FN: 1; TN: 1.
Boundary & unit check (Space Ether Strain 6 1 5 True False False True True True 40 3 15 11 0 99 1 0 0 Ai Overview)
When solving for 'space ether strain 6 1 5 true false false true true true 40 3 15 11 0 99 1 0 0 ai overview', verify that Input mode, True positives, False positives use consistent units and stay within the documented scope: Labels are 0 or 1, with 1 as the positive class. Accuracy, precision, recall and F1 use standard binary counts. A zero metric denominator is reported as undefined; total observation count must be positive. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
How to Read and Verify the Confusion Matrix Calculator Output
The output panel reports the exact result derived from Input mode, True positives, False positives alongside any unit or formatting notes.
Every calculation follows the rule: Build a binary confusion matrix from counts or paired labels and calculate its basic metrics. Because JavaScript uses IEEE 754 double-precision floating-point arithmetic, numeric outputs display up to 12 significant digits where applicable.
Always cross-check the displayed result against the scope note (Labels are 0 or 1, with 1 as the positive class. Accuracy, precision, recall and F1 use standard binary counts. A zero metric denominator is reported as undefined; total observation count must be positive. Numeric output uses up to 12 significant digits; floating-point rounding can occur) before applying the value to production specifications, financial models, or lab records.
Scope Limits, Accuracy Assumptions & Related Everyday Math Tools
Review these boundary constraints and validation rules before relying on the Confusion Matrix Calculator output.
Operational scope: Labels are 0 or 1, with 1 as the positive class. Accuracy, precision, recall and F1 use standard binary counts. A zero metric denominator is reported as undefined; total observation count must be positive. Numeric output uses up to 12 significant digits; floating-point rounding can occur. Inputs that violate minimum/maximum bounds or required format rules are rejected with a clear diagnostic prompt.
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Open toolFrequently Asked Questions About Confusion Matrix Calculator
Answers about using Confusion Matrix Calculator and understanding its results.
How do I calculate space ether strain 6 1 5 true false false true true true 40 3 15 11 0 99 1 0 0 ai overview using the Confusion Matrix Calculator?
The Confusion Matrix Calculator applies the rule: Build a binary confusion matrix from counts or paired labels and calculate its basic metrics. Enter Input mode, True positives, False positives into the workspace and the tool computes the exact output in your browser.
Can I use this tool for confusion matrix calculator?
Yes. Provide Input mode, True positives, False positives using the documented units and format shown in the quick reference table above. Preloaded default values let you test the workflow immediately.
What are the accuracy limits and boundary assumptions for Confusion Matrix Calculator?
Labels are 0 or 1, with 1 as the positive class. Accuracy, precision, recall and F1 use standard binary counts. A zero metric denominator is reported as undefined; total observation count must be positive. Numeric output uses up to 12 significant digits; floating-point rounding can occur. Confirm that your inputs fall within the allowed ranges before exporting the result.
Is the Confusion Matrix Calculator free and does it keep my inputs private?
Yes. All calculations for the Confusion Matrix Calculator execute 100% locally in your browser's JavaScript runtime. No inputs or results are uploaded to any external server.
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