Binary Predicted Label Count Balance

Description

Sum supplied binary confusion cells into predicted-label totals.

Classification terms depend on label vocabularies, decision thresholds and denominator definitions. One class recall is not a full balanced-accuracy metric, and a row or column total is not automatically a precision or recall denominator. Empty or zero-total cases need an explicit defined flag or convention.

Sum supplied binary confusion cells into predicted-label totals.

When to use this tool

Use Binary Predicted Label Count Balance when you already have truePositive, falsePositive, falseNegative, trueNegative and need to sum supplied binary confusion cells into predicted-label totals.

How to use the tool and how it works

Enter truePositive, falsePositive, falseNegative, trueNegative. Use the units and conventions below.
Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
Read predictedPositive, predictedNegative, total. Use the output convention below.

Require nonnegative safe TP/FP/FN/TN; use BigInt sums for TP+FP, FN+TN and all four, then export safe integers.^[1]

Input field	Meaning and units	Accepted input
truePositive	True Positive	Required. Safe integer; minimum 0.
falsePositive	False Positive	Required. Safe integer; minimum 0.
falseNegative	False Negative	Required. Safe integer; minimum 0.
trueNegative	True Negative	Required. Safe integer; minimum 0.

Output field	Interpretation	Returned shape
predictedPositive	Predicted Positive	Required. Safe integer.
predictedNegative	Predicted Negative	Required. Safe integer.
total	Exact sum of four counts, required safe along with exported predicted totals.	Required. Safe integer.

json

{
  "input": {
    "truePositive": 30,
    "falsePositive": 5,
    "falseNegative": 10,
    "trueNegative": 55
  },
  "output": {
    "predictedPositive": 35,
    "predictedNegative": 65,
    "total": 100
  }
}

Limitations and assumptions

Any unsafe output count is rejected. No actual predictions, labels, positive-class convention, sample independence or model quality is verified.
Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.

Alternative or Complementary methods

Evaluate the complete prediction set, confusion structure and decision policy. Report missingness, class scope and uncertainty with the full metric; local threshold flags or one-cell fractions cannot characterize overall model performance.

References

Calculation and input contract: classification/binary-predicted-label-count-balance — tool.io

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